Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.
Computer-based representation of physical systems and phenomena such as chemical processes.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
The process of cumulative change over successive generations through which organisms acquire their distinguishing morphological and physiological characteristics.
The pattern of any process, or the interrelationship of phenomena, which affects growth or change within a population.
The study of chance processes or the relative frequency characterizing a chance process.
A functional system which includes the organisms of a natural community together with their environment. (McGraw Hill Dictionary of Scientific and Technical Terms, 4th ed)
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)
The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
The relationships of groups of organisms as reflected by their genetic makeup.
Differential and non-random reproduction of different genotypes, operating to alter the gene frequencies within a population.
Elements of limited time intervals, contributing to particular results or situations.
Genotypic differences observed among individuals in a population.
Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations.
The rate dynamics in chemical or physical systems.
The process of pictorial communication, between human and computers, in which the computer input and output have the form of charts, drawings, or other appropriate pictorial representation.
The portion of an interactive computer program that issues messages to and receives commands from a user.
Sequential operating programs and data which instruct the functioning of a digital computer.
A loose confederation of computer communication networks around the world. The networks that make up the Internet are connected through several backbone networks. The Internet grew out of the US Government ARPAnet project and was designed to facilitate information exchange.
The visual display of data in a man-machine system. An example is when data is called from the computer and transmitted to a CATHODE RAY TUBE DISPLAY or LIQUID CRYSTAL display.
A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.
Use for articles on the investing of funds for income or profit.
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
A phase transition from liquid state to gas state, which is affected by Raoult's law. It can be accomplished by fractional distillation.
The biosynthesis of RNA carried out on a template of DNA. The biosynthesis of DNA from an RNA template is called REVERSE TRANSCRIPTION.
Endogenous substances, usually proteins, which are effective in the initiation, stimulation, or termination of the genetic transcription process.
Biological activities of viruses and their interactions with the cells they infect.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control (induction or repression) of gene action at the level of transcription or translation.
DNA sequences which are recognized (directly or indirectly) and bound by a DNA-dependent RNA polymerase during the initiation of transcription. Highly conserved sequences within the promoter include the Pribnow box in bacteria and the TATA BOX in eukaryotes.
The selecting and organizing of visual stimuli based on the individual's past experience.
Area of the OCCIPITAL LOBE concerned with the processing of visual information relayed via VISUAL PATHWAYS.
The process by which the nature and meaning of sensory stimuli are recognized and interpreted.
Investigative technique commonly used during ELECTROENCEPHALOGRAPHY in which a series of bright light flashes or visual patterns are used to elicit brain activity.
The electric response evoked in the cerebral cortex by visual stimulation or stimulation of the visual pathways.

A processive single-headed motor: kinesin superfamily protein KIF1A. (1/2792)

A single kinesin molecule can move "processively" along a microtubule for more than 1 micrometer before detaching from it. The prevailing explanation for this processive movement is the "walking model," which envisions that each of two motor domains (heads) of the kinesin molecule binds coordinately to the microtubule. This implies that each kinesin molecule must have two heads to "walk" and that a single-headed kinesin could not move processively. Here, a motor-domain construct of KIF1A, a single-headed kinesin superfamily protein, was shown to move processively along the microtubule for more than 1 micrometer. The movement along the microtubules was stochastic and fitted a biased Brownian-movement model.  (+info)

Influence of sampling on estimates of clustering and recent transmission of Mycobacterium tuberculosis derived from DNA fingerprinting techniques. (2/2792)

The availability of DNA fingerprinting techniques for Mycobacterium tuberculosis has led to attempts to estimate the extent of recent transmission in populations, using the assumption that groups of tuberculosis patients with identical isolates ("clusters") are likely to reflect recently acquired infections. It is never possible to include all cases of tuberculosis in a given population in a study, and the proportion of isolates found to be clustered will depend on the completeness of the sampling. Using stochastic simulation models based on real and hypothetical populations, the authors demonstrate the influence of incomplete sampling on the estimates of clustering obtained. The results show that as the sampling fraction increases, the proportion of isolates identified as clustered also increases and the variance of the estimated proportion clustered decreases. Cluster size is also important: the underestimation of clustering for any given sampling fraction is greater, and the variability in the results obtained is larger, for populations with small clusters than for those with the same number of individuals arranged in large clusters. A considerable amount of caution should be used in interpreting the results of studies on clustering of M. tuberculosis isolates, particularly when sampling fractions are small.  (+info)

A fast, stochastic threading algorithm for proteins. (3/2792)

MOTIVATION: Sequences for new proteins are being determined at a rapid rate, as a result of the Human Genome Project, and related genome research. The ability to predict the three-dimensional structure of proteins from sequence alone would be useful in discovering and understanding their function. Threading, or fold recognition, aims to predict the tertiary structure of a protein by aligning its amino acid sequence with a large number of structures, and finding the best fit. This approach depends on obtaining good performance from both the scoring function, which simulates the free energy for given trial alignments, and the threading algorithm, which searches for the lowest-score alignment. It appears that current scoring functions and threading algorithms need improvement. RESULTS: This paper presents a new threading algorithm. Numerical tests demonstrate that it is more powerful than two popular approximate algorithms, and much faster than exact methods.  (+info)

Declining survival probability threatens the North Atlantic right whale. (4/2792)

The North Atlantic northern right whale (Eubalaena glacialis) is considered the most endangered large whale species. Its population has recovered only slowly since the cessation of commercial whaling and numbers about 300 individuals. We applied mark-recapture statistics to a catalog of photographically identified individuals to obtain the first statistically rigorous estimates of survival probability for this population. Crude survival decreased from about 0.99 per year in 1980 to about 0.94 in 1994. We combined this survival trend with a reported decrease in reproductive rate into a branching process model to compute population growth rate and extinction probability. Population growth rate declined from about 1. 053 in 1980 to about 0.976 in 1994. Under current conditions the population is doomed to extinction; an upper bound on the expected time to extinction is 191 years. The most effective way to improve the prospects of the population is to reduce mortality. The right whale is at risk from entanglement in fishing gear and from collisions with ships. Reducing this human-caused mortality is essential to the viability of this population.  (+info)

FORESST: fold recognition from secondary structure predictions of proteins. (5/2792)

MOTIVATION: A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best. RESULTS: After model cross-validation, the success rate on 44 test proteins covering the three structural classes was found to be 59%. On seven fold predictions performed prior to the publication of experimental structure, the success rate was 71%. In conclusion, this approach manages to capture important information about the fold of a protein embedded in the length and arrangement of the predicted helices, strands and coils along the polypeptide chain. When a more extensive library of HMMs representing the universe of known structural families is available (work in progress), the program will allow rapid screening of genomic databases and sequence annotation when fold similarity is not detectable from the amino acid sequence. AVAILABILITY: FORESST web server at http://absalpha.dcrt.nih.gov:8008/ for the library of HMMs of structural families used in this paper. FORESST web server at http://www.tigr.org/ for a more extensive library of HMMs (work in progress). CONTACT: [email protected]; [email protected]; [email protected]  (+info)

A quantitative method for the detection of edges in noisy time-series. (6/2792)

A modification of the edge detector of Chung & Kennedy is proposed in which the output provides confidence limits for the presence or absence of sharp edges (steps) in the input waveform. Their switching method with forward and backward averaging windows is retained, but the output approximates an ideal output function equal to the difference in these averages divided by the standard deviation of the noise. Steps are associated with peak output above a pre-set threshold. Formulae for the efficiency and reliability of this ideal detector are derived for input waveforms with Gaussian white noise and sharp edges, and serve as benchmarks for the switching edge detector. Efficiency is kept high if the threshold is a fixed fraction of the step size of interest relative to noise, and reliability is improved by increasing the window width W to reduce false output. For different steps sizes D, the window width for fixed efficiency and reliability scales as 1/D2. Versions with weighted averaging (flat, ramp, triangular) or median averaging but the same window width perform similarly. Binned above-threshold output is used to predict the locations and signs of detected steps, and simulations show that efficiency and reliability are close to ideal. Location times are accurate to order square root of W. Short pulses generate reduced output if the number of data points in the pulse is less than W. They are optimally detected by choosing W as above and collecting data at a rate such that the pulse contains approximately W data points. A Fortran program is supplied.  (+info)

Strength of a weak bond connecting flexible polymer chains. (7/2792)

Bond dissociation under steadily rising force occurs most frequently at a time governed by the rate of loading (Evans and Ritchie, 1997 Biophys. J. 72:1541-1555). Multiplied by the loading rate, the breakage time specifies the force for most frequent failure (called bond strength) that obeys the same dependence on loading rate. The spectrum of bond strength versus log(loading rate) provides an image of the energy landscape traversed in the course of unbonding. However, when a weak bond is connected to very compliant elements like long polymers, the load applied to the bond does not rise steadily under constant pulling speed. Because of nonsteady loading, the most frequent breakage force can differ significantly from that of a bond loaded at constant rate through stiff linkages. Using generic models for wormlike and freely jointed chains, we have analyzed the kinetic process of failure for a bond loaded by pulling the polymer linkages at constant speed. We find that when linked by either type of polymer chain, a bond is likely to fail at lower force under steady separation than through stiff linkages. Quite unexpectedly, a discontinuous jump can occur in bond strength at slow separation speed in the case of long polymer linkages. We demonstrate that the predictions of strength versus log(loading rate) can rationalize conflicting results obtained recently for unfolding Ig domains along muscle titin with different force techniques.  (+info)

Visual form created solely from temporal structure. (8/2792)

In several experiments, it was found that global perception of spatial form can arise exclusively from unpredictable but synchronized changes among local features. Within an array of nonoverlapping apertures, contours move in one of two directions, with direction reversing randomly over time. When contours within a region of the array reverse directions in synchrony, they stand out conspicuously from the rest of the array where direction reversals are unsynchronized. Clarity of spatial structure from synchronized change depends on the rate of motion reversal and on the proportion of elements reversing direction in synchrony. Evidently, human vision is sensitive to the rich temporal structure in these stochastic events.  (+info)

Some problems are analyzed arising when a numerical simulation of a random motion of a large ensemble of diffusing particles is used to approximate the solution of a one-dimensional diffusion equation. The particle motion is described by means of a stochastic differential equation. The problems emerging especially when the diffusion coefficient is a function of spatial coordinate are discussed. The possibility of simulation of various kinds of stochastic integral is demonstrated. It is shown that the application of standard numerical procedures commonly adopted for ordinary differential equations may lead to erroneous results when used for solution of stochastic differential equations. General conclusions are verified by numerical solution of three stochastic differential equations with different forms of the diffusion coefficient.. Keywords: Stochastic modelling; Diffusion process; Stochastic differential equation. ...
Starting with the construction of stochastic processes, the book introduces Brownian motion and martingales. After proving the Doob-Meyer decomposition, quadratic variation processes and local ... More. Starting with the construction of stochastic processes, the book introduces Brownian motion and martingales. After proving the Doob-Meyer decomposition, quadratic variation processes and local martingales are discussed. The book proceeds to construct stochastic integrals, prove the Itô formula, derive several important applications of the formula such as the martingale representation theorem and the Burkhölder-Davis-Gundy inequality, and establish the Girsanov theorem on change of measures. Next, attention is focused on stochastic differential equations which arise in modeling physical phenomena, perturbed by random forces. Diffusion processes are solutions of stochastic differential equations and form the main theme of this book. After establishing the existence and uniqueness of strong ...
Several stochastic simulation algorithms (SSAs) have been recently proposed for modelling reaction-diffusion processes in cellular and molecular biology. In this talk, two commonly used SSAs will be studied. The first SSA is an on-lattice model described by the reaction-diffusion master equation. The second SSA is an off-lattice model based on the simulation of Brownian motion of individual molecules and their reactive collisions. The connections between SSAs and the deterministic models (based on reaction-diffusion PDEs) will be presented. I will consider chemical reactions both at a surface and in the bulk. I will show how the microscopic parameters should be chosen to achieve the correct macroscopic reaction rate. This choice is found to depend on which SSA is used. I will also present multiscale algorithms which use models with a different level of detail in different parts of the computational domain ...
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A modeling approach to treat noisy engineering systems is presented. We deal with controlled systems that evolve in a continuous-time over finite time intervals, but also in continuous interaction with environments of intrinsic variability. We face the compl A modeling approach to treat noisy engineering systems is presented. We deal with controlled systems that evolve in a continuous-time over finite time intervals, but also in continuous interaction with environments of intrinsic variability. We face the complexity of these systems by introducing a methodology based on Stochastic Differential Equations (SDE) models. We focus on specific type of complexity derived from unpredictable abrupt and/or structural changes. In this paper an approach based on controlled Stochastic Differential Equations with Markovian Switchings (SDEMS) is proposed. Technical conditions for the existence and uniqueness of the solution of these models are provided. We treat with nonlinear SDEMS that does not have closed ...
Arnold, L. (1975): Stochastic Differential Equations New York, John Wiley and Sons.. Bianchi, C., R. Cesari and L. Panattoni (1995): Alternative Estimators of the Cox, Ingersoll and Ross Model of the term Structure of Interest Rates. Roma, Banca dItalia, Temi di Discussione N.326.. Bianchi, C. and E. M. Cleur (1996, forthcomning): Indirect Estimation of Stochastic Differential Equation Models: Some Computational Experiment, Computational Statistics.. Brennan, M.J. and E.S. Schwartz (1979): A Continuous Time Approach to the Pricing of Bonds, Journal of Banking and Finance, 3, 135-155.. Broze, L., O. Scaillet and J.M. Zakoian (1994): Quasi Indirect Inference for Diffusion Processes. Paris: Crest, document de travail No.9511.. Broze, L., O. Scaillet and J.M. Zakoian (1995): Testing for Continuous Time Models of the Short-Term Interest Rate Journal of Empirical Finance, 2, 199-223.. Calzolari, G. (1979): Antithetic Variates to Estimate the Simulation Bias in Non-Linear Models, Economics Letters 4, ...
|p style=text-indent:20px;|When solving linear stochastic differential equations numerically, usually a high order spatial discretisation is used. Balanced truncation (BT) and singular perturbation approximation (SPA) are well-known projection techniques in the deterministic framework which reduce the order of a control system and hence reduce computational complexity. This work considers both methods when the control is replaced by a noise term. We provide theoretical tools such as stochastic concepts for reachability and observability, which are necessary for balancing related model order reduction of linear stochastic differential equations with additive Lévy noise. Moreover, we derive error bounds for both BT and SPA and provide numerical results for a specific example which support the theory.|/p|
1] M.I. Freidlin, Functional Integration and Partial Differential Equations, Princeton University Press, Princeton, 1985. , MR 833742 , Zbl 0568.60057 [2] A. Friedman, Stochastic Differential Equations, Vol. 1, 1975, Academic Press. , Zbl 0323.60056 [3] K. Ichihara, Some Global Properties of Symmetric Diffusion Processes, Publ. RIMS Kyoto Univ., Vol. 14, 1978, pp. 441-486. , MR 509198 , Zbl 0397.60062 [4] R.Z. Khasminkii, Ergodic Properties of Recurrent Diffusion Processes and Stabilization of the Solution of the Cauchy Problem for Parabolic Equations, Proc. Theory and Appl., Vol. 5, 1960, pp. 179-196. , MR 133871 , Zbl 0106.12001 [5] R.Z. Khasminskii, On the Averaging Principle for Stochastic Differential Equations, Kybernetika, Academia, Praha, Vol. 4, 1968, pp. 260-279 (Russian). , MR 260052 , Zbl 0231.60045 [6] M.A. Pinsky and R.G. Pinsky, Transience and Recurrence for Diffusions in Random Temporal Environments, Annals of Probability (to appear). [7] R.G. Pinsky, Transience and Recurrence ...
ESAIM: Control, Optimisation and Calculus of Variations (ESAIM: COCV) publishes rapidly and efficiently papers and surveys in the areas of control, optimisation and calculus of variations
CiteSeerX - Scientific documents that cite the following paper: Stochastic simulation algorithms for dynamic probabilistic networks
Author summary Genetically identical cells, even when they are exposed to the same environmental conditions, display incredible diversity. Gene expression noise is attributed to be a key source of this phenotypic diversity. Transcriptional dynamics is a dominant source of expression noise. Although scores of theoretical and experimental studies have explored how noise is regulated at the level of transcription, most of them focus on the gene specific, cis regulatory elements, such as the number of transcription factor (TF) binding sites, their binding strength, etc. However, how the global properties of transcription, such as the limited availability of TFs impact noise in gene expression remains rather elusive. Here we build a theoretical model that incorporates the effect of limiting TF pool on gene expression noise. We find that competition between genes for TFs leads to enhanced variability in mRNA copy number across an isogenic population. Moreover, for gene copies sharing TFs with other competitor
In this paper, we develop a stochastic differential equation model to simulate the movement of a social/subsocial spider species, |em|Anelosimus studiosus|/em|, during prey capture using experimental data collected in a structured environment. In a subsocial species, females and their maturing offspring share a web and cooperate in web maintenance and prey capture. Furthermore, observations indicate these colonies change their positioning throughout the day, clustered during certain times of the day while spaced out at other times. One key question was whether or not the spiders spaced out ``optimally to cooperate in prey capture. In this paper, we first show the derivation of the model where experimental data is used to determine key parameters within the model. We then use this model to test the success of prey capture under a variety of different spatial configurations for varying colony sizes to determine the best spatial configuration for prey capture.
Background. The chemical master equation is the fundamental equation of stochastic chemical kinetics. This differential-difference equation describes temporal evolution of the probability density function for states of a chemical system. A state of the system, usually encoded as a vector, represents the number of entities or copy numbers of interacting species, which are changing according to a list of possible reactions. It is often the case, especially when the state vector is high-dimensional, that the number of possible states the system may occupy is too large to be handled computationally. One way to get around this problem is to consider only those states that are associated with probabilities that are greater than a certain threshold level. Results. We introduce an algorithm that significantly reduces computational resources and is especially powerful when dealing with multi-modal distributions. The algorithm is built according to two key principles. Firstly, when performing time ...
Accurate modeling of reaction kinetics is important to understand how biological cells work. Spatially well-mixed reaction dynamics can be modeled by the chemical master equation (CME, see formula 2), an infinite set of ordinary differential equations, which is, in general, too complex to be solved analytically. There are accurate numerical simulation schemes for solving the CME indirectly, like Gillespies stochastic simulation algorithm (FN:D. T. Gillespie. Exact Stochastic Simulation of Coupled Chemical Reactions. Journal of Physical Chemistry, 81[25]:2340-2361, 1977.). For many relevant realistic settings, however, even our high-performance computers fail to create reliable statistics within an acceptable amount of time. This is the motivation to reduce the model complexity by considering approximative mathematical formulations of the cellular dynamics. Especially multiscale reaction systems, which often appear in real-world applications, are in the focus of our investigations because they ...
An old and important problem in the field of nonlinear time-series analysis entails the distinction between chaotic and stochastic dynamics. Recently, e-recurrence networks have been proposed as a tool to analyse the structural properties of a time series. In this paper, we propose the applicability of local and global e-recurrence network measures to distinguish between chaotic and stochastic dynamics using paradigmatic model systems such as the Lorenz system, and the chaotic and hyper-chaotic Rossler system. We also demonstrate the effect of increasing levels of noise on these network measures and provide a real-world application of analysing electroencephalographic data comprising epileptic seizures. Our results show that both local and global e-recurrence network measures are sensitive to the presence of unstable periodic orbits and other structural features associated with chaotic dynamics that are otherwise absent in stochastic dynamics. These network measures are still robust at high ...
A balanced approach to probability, statistics, stochastic models, and stochastic differential equations with special emphasis on engineering applications. Random variables, probability distributions, Monte Carlo simulations models, statistical inference theory, design of engineering experiments, reliability and risk assessment, fitting data to probability distributions, ANOVA, stochastic processes, Brownian motion, white noise, random walk, colored noise processes. Differential equations subject to random initial conditions, random forcing functions, and random parameters. Partial differential equations subject to stochastic boundary conditions. New techniques for non-linear differential equations. Computer simulation with MAPLE and other symbolic algebra software. 0520. Introduction to Bioengineering (3 s.h.) ...
InCharge,author1=Christoph Hauert}} {{TOCright}} Stochastic differential equations (SDE) provide a general framework to describe the evolutionary dynamics of an arbitrary number of strategic types \(d\) in finite populations, which results in demographic noise, as well as to incorporate mutations. For large, but finite populations this allows to include demographic noise without requiring explicit simulations. Instead, the population size only rescales the amplitude of the noise. Moreover, this framework admits the inclusion of mutations between different types, provided that mutation rates, \(\mu\), are not too small compared to the inverse population size \(1/N\). This ensures that all types are almost always represented in the population and that the occasional extinction of one type does not result in an extended absence of that type. For \(\mu N\ll1\) this limits the use of SDEs, but in this case well established alternative approximations are available based on time scale separation. The ...
InCharge,author1=Christoph Hauert}} {{TOCright}} Stochastic differential equations (SDE) provide a general framework to describe the evolutionary dynamics of an arbitrary number of strategic types \(d\) in finite populations, which results in demographic noise, as well as to incorporate mutations. For large, but finite populations this allows to include demographic noise without requiring explicit simulations. Instead, the population size only rescales the amplitude of the noise. Moreover, this framework admits the inclusion of mutations between different types, provided that mutation rates, \(\mu\), are not too small compared to the inverse population size \(1/N\). This ensures that all types are almost always represented in the population and that the occasional extinction of one type does not result in an extended absence of that type. For \(\mu N\ll1\) this limits the use of SDEs, but in this case well established alternative approximations are available based on time scale separation. The ...
The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy.. ...
While the birth-death model is, in itself, inappropriate for representing intracellular bacteria (§1), it has provided a useful foundation for the birth-death-survival model considered here. In the 1960s, there was considerable academic interest in the mathematics of the simple birth-death model, involving stochastic differential equations [14,15] and generating functions [48]. However, very few experimental studies have actually made use of these results, despite a thorough account [23] crediting their ability in representing data for a variety of diseases. That paper [23] and methods therein are however not without their critics. It is claimed [5] that while the overall picture provided by the basic birth-death model corresponds remarkably well to what is found in practice, the underlying interpretations are flawed and there is no experimental evidence to suggest any form of stochastic mechanism in the infection dynamics. However, this is later refuted in an in vivo study [40] (in which ...
TY - ABST. T1 - Stochastic simulation model for spatial-temporal development of a fungal plant disease spread by wind. AU - Østergård, Hanne. PY - 2003. Y1 - 2003. KW - 9-B risiko. M3 - Conference abstract for conference. T2 - Dina workshop on dispersal models with agricultural applications. Y2 - 1 January 2003. ER - ...
Real-time in situ operation of bio/chemical sensors assumes detection of chemical substances or biological specimens in samples of complex composition. Since sensor selectivity cannot be ideal, adsorption of particles other than target particles inevitably occur on the sensing surface. That affects the sensor response and its intrinsic fluctuations which are caused by stochastic fluctuations of the numbers of adsorbed particles of all the adsorbing substances. In microfluidic sensors, such response fluctuations are a result of coupled adsorption, desorption and mass transfer (convection and diffusion) processes of analyte particles. Analysis of these fluctuations is important because they constitute the adsorption-desorption noise, which limits the sensing performance. In this work we perform the analysis of fluctuations by using a stochastic model of sensor response after the steady state is reached, in the case of two-analyte adsorption, considering mass transfer processes. The resul...ts ...
This page collects some information about stochastic systems courses offered at Caltech. This page was prepared in preparation for a faculty discussion on the current stochastic systems sequence (ACM/EE 116, ACM 216, ACM 217/EE 164). == Introduction == === Background === The current sequence of courses (ACM/EE 116, ACM 216, ACM 217/EE 164) were first offered in the 2005-06 academic year following discussions between Emmanuel Candes, Babak Hassibi, Jerry Marsden, Richard Murray and Houman Ohwadi about how to integrate some of the course offerings in ACM and EE, with an eye toward applications in CDS. EE 162 (Random Processes for Communication and Signal Processing) was eliminated and replaced by ACM/EE 116. There are three drivers for evaluating the course sequence at this time: * Its been a while since we set this up and it would be good to get together and see what we think about how its been going. * CDS is about to require ACM 116 as part of its PhD requirements (in place of CDS 140b) and ...
Methods to implement stochastic simulations on the graphics processing unit (GPU) have been developed. These algorithms are used in a simulation of microassembly and nanoassembly with optical tweezers, but are also directly compatible with simulations of a wide variety of assembly techniques using either electrophoretic, magnetic, or other trapping techniques. Significant speedup is possible for stochastic particle simulations when using the GPU, included in most personal computers (PCs), rather than the central processing unit (CPU) that handles most calculations. However, a careful analysis of the accuracy and precision when using the GPU in stochastic simulations is lacking and is addressed here. A stochastic simulation for spherical particles has been developed and mapped onto stages of the GPU hardware that provide the best performance. The results from the CPU and GPU implementation are then compared with each other and with well-established theory. The error in the mean ensemble energy ...
Research and Markets: Stochastic Simulation and Applications in Finance with MATLAB: DUBLIN, Ireland--(BUSINESS WIRE)--January 23, 2009-- Research and Markets (http://www.researchandmarkets.com/research/40d9cd/stochastic_simulat) has announced the addition of John Wiley and Sons Ltds new report &Stochastic Simulation and Applications in Finance with MATLAB Programs& to their offering. Stochastic Simulation and Applications in Finance with MATLAB Programs explains the …
The workshop will focus on Rough Path Analysis and its rapidly growing applications in Applied Stochastic Analysis, ranging from the resolution of ill-posed stochastic partial differential equations to new ways of handling highdimensional data. ...
The development of plants impresses us with the well-orchestrated formation of tissues and structures throughout the lifetime of the organism, despite its constituents being inherently stochastic. At first glance the prevalent noise on the molecular level seems hard to reconcile with the robustness and reproducibility of development. How is stochastic variability overcome during development and developmental decision-making? When is stochasticity employed to generate patterns? How can stochastic events drive a process? How do lower level stochastic fluctuations affect development at more global levels? Stochastic variability is prevalent whenever low molecule numbers and/or small system sizes are involved. Especially during development a few cells are at the foundation of a growing organ, and the stochastic dynamics of regulatory molecules drive spatiotemporal specification of structures to be. Stochasticity is emerging as an important factor in the regulation of diverse plant developmental
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Stochastic analysis of steady-state two-phase (water and oil) flow in heterogeneous porous media is performed using the perturbation theory and spectral representation techniques. The governing equations describing the flow are coupled and nonlinear. The key stochastic input variables are intrinsic permeability,k, and the soil and fluid dependent retention parameter, г. Three different stochastic combinations of these two imput parameters were considered. The perturbation/spectral analysis was used to develop closed-form expressions that describe stochastic variability of key output processes, such as capillary and individual phase pressures and specific discharges. The analysis also included the estimation of the effective flow properties. The impact of the spatial variability ofk and г on the variances of pressures, effective conductivities, and specific discharges was examined.
Stochastic Analysis of Neural Spike Count Dependencies [Elektronische Ressource] / Arno Onken. Betreuer: Klaus Obermayer : Technische Universitat Berlin¨Stochastic Analysisof Neural Spike Count Dependenciesvorgelegt vonDiplom-InformatikerArno Onkenaus AurichVon der Fakultat IV - Elektrotechnik und Informatik¨der Technischen Universitat Berlin¨zur Erlangung des akademischen GradesDoktor der NaturwissenschaftenDr. rer. nat.genehmigte DissertationPromotionsausschuss:Vorsitzender: Prof. Dr. Klaus-Robert Mu¨llerBerichter:
The global and stochastic analysis with applications of writers that reorients under this hand holds here removed from a nuclear gross misconfigured and other carbohydrates to understand any following of others that are or are biochemical concepts between a diplomatic East and West. This bibliographicum does writings to some common participants in other archived Year through the Medusa of the Silk Road. We will run also at a instructor of Silk Road approaches and their endurance to discrete novel or theoretical presentations, and think the traditions in which queries are rewritten, read, and only measured biblical rights in happening global conversation. We will partly compare compounds of global and stochastic D-galactose, writing, and Literary and late plays. Bildungsroman, global of epics, urgent fascination). The fast traffic for this narratology occurs adaptations by Rousseau, Goethe, unknown, and Tolstoy. We well are powerful diseases by Georg Lukacs, Franco Moretti, Clement Lugowski, ...
Gene expression within cells is known to fluctuate stochastically in time. However, the origins of gene expression noise remain incompletely understood. The bacterial cell cycle has been suggested as one source, involving chromosome replication, exponential volume growth, and various other changes in cellular composition. Elucidating how these factors give rise to expression variations is important to models of cellular homeostasis, fidelity of signal transmission, and cell-fate decisions. Using single-cell time-lapse microscopy, we measured cellular growth as well as fluctuations in the expression rate of a fluorescent protein and its concentration. We found that, within the population, the mean expression rate doubles throughout the cell cycle with a characteristic cell cycle phase dependent shape which is different for slow and fast growth rates. At low growth rate, we find the mean expression rate was initially flat, and then rose approximately linearly by a factor two until the end of the cell
Generation and filtering of gene expression noise by the bacterial cell cycle. . Biblioteca virtual para leer y descargar libros, documentos, trabajos y tesis universitarias en PDF. Material universiario, documentación y tareas realizadas por universitarios en nuestra biblioteca. Para descargar gratis y para leer online.
We derive the mean-field equations arising as the limit of a network of interacting spiking neurons, as the number of neurons goes to infinity. The neurons belong to a fixed number of populations and are represented either by the Hodgkin-Huxley model or by one of its simplified version, the FitzHugh-Nagumo model. The synapses between neurons are either electrical or chemical. The network is assumed to be fully connected. The maximum conductances vary randomly. Under the condition that all neurons initial conditions are drawn independently from the same law that depends only on the population they belong to, we prove that a propagation of chaos phenomenon takes place, namely that in the mean-field limit, any finite number of neurons become independent and, within each population, have the same probability distribution. This probability distribution is a solution of a set of implicit equations, either nonlinear stochastic differential equations resembling the McKean-Vlasov equations or non-local partial
Finden Sie alle Bücher von Moisés Santillán - Chemical Kinetics, Stochastic Processes, and Irreversible Thermodynamics. Bei der Büchersuchmaschine eurobuch.de können Sie antiquarische und Neubücher VERGLEICHEN UND SOFORT zum Bestpreis bestellen. 9783319066882
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). Numerical analysis naturally finds application in all fields of engineering and the physical sciences, but in the 21st century also the life sciences, social sciences, medicine, business and even the arts have adopted elements of scientific computations. The growth in computing power has revolutionized the use of realistic mathematical models in science and engineering, and subtle numerical analysis is required to implement these detailed models of the world. For example, ordinary differential equations appear in celestial mechanics (predicting the motions of planets, stars and galaxies); numerical linear algebra is important for data analysis; stochastic differential equations and Markov chains are essential in simulating living cells for medicine and biology. Before the advent of modern ...
Population systems are often subject to environmental noise, and our aim is to show that (surprisingly) the presence of even a tiny amount can suppress a potential population explosion. To prove this intrinsically interesting result, we stochastically perturb the multivariate deterministic system ẋ(t) = f(x(t)) into the Itô form dx(t) = f(x(t))dt + g(x(t))dw(t), and show that although the solution to the original ordinary differential equation may explode to infinity in a finite time, with probability one that of the associated stochastic differential equation does not.. ...
Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacteriums life cycle- Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or ...
All populations fluctuate stochastically, creating a risk of extinction that does not exist in deterministic models, with fundamental consequences for both pure and applied ecology. This book provides an introduction to stochastic population dynamics, combining classical background material with a variety of modern approaches, including previously unpublished results by the authors, illustrated with examples from bird and mammal populations, and insect communities. Demographic and environmental stochasticity are introduced with statistical methods for estimating them from field data. The long-run growth rate of a population is explained and extended to include age structure with both demographic and environmental stochasticity. Diffusion approximations facilitate the analysis of extinction dynamics and the duration of the final decline. Methods are developed for estimating delayed density dependence from population time series using life history data. Metapopulation viability and the spatial scale of
Randomness is an important component of modeling complex phenomena in biological, chemical, physical, and engineering systems. Based on many years teaching this material, Jinqiao Duan develops a modern approach to the fundamental theory and application of stochastic dynamical systems for applied mathematicians and quantitative engineers and scientists. The highlight is the staged development of invariant stochastic structures that underpin much of our understanding of nonlinear stochastic systems and associated properties such as escape times. The book ranges from classic Brownian motion to noise generated by α-stable Levy flights. A. J. Roberts, University of Adelaide. This book provides a beautiful concise introduction to the flourishing field of stochastic dynamical systems, successfully integrating the exposition of important technical concepts with illustrative and insightful examples and interesting remarks regarding the simulation of such systems. Both presentation style and content ...
This book presents the proceedings from the International Conference held in Halifax, NS in July 1997. Funded by The Fields Institute and Le Centre de Recherches Mathématiques, the conference was held in honor of the retirement of Professors Lynn Erbe and Herb I. Freedman (University of Alberta). Featured topics include ordinary, partial, functional, and stochastic differential equations and their applications to biology, epidemiology, neurobiology, physiology and other related areas ...
By a novel approach, we get some explicit criteria for the mean square exponential stability of linear stochastic differential equations with distributed delays. Stability criteria presented in this...
Expansions in Generalized Eigenfunctions of the Weighted Laplacian on Star-shaped Networks -- Diffusion Equations with Finite Speed of Propagation -- Subordinated Multiparameter Groups of Linear Operators: Properties via the Transference Principle -- An Integral Equation in AeroElasticity -- Eigenvalue Asymptotics Under a Non-dissipative Eigenvalue Dependent Boundary Condition for Second-order Elliptic Operators -- Feynman-Kac Formulas, Backward Stochastic Differential Equations and Markov Processes -- Generation of Cosine Families on L p (0,1) by Elliptic Operators with Robin Boundary Conditions -- Global Smooth Solutions to a Fourth-order Quasilinear Fractional Evolution Equation -- Positivity Property of Solutions of Some Quasilinear Elliptic Inequalities -- On a Stochastic Parabolic Integral Equation -- Resolvent Estimates for a Perturbed Oseen Problem -- Abstract Delay Equations Inspired by Population Dynamics -- Weak Stability for Orbits of C 0-semigroups on Banach Spaces -- Contraction ...
This article is concerned with the fluctuation analysis and the stability properties of a class of one-dimensional Riccati diffusions. This class of Riccati diffusion is quite general, and arises, for example, in data assimilation applications, and more particularly in ensemble (Kalman-type) filtering theory. These one-dimensional stochastic differential equations exhibit a quadratic drift function and a non-Lipschitz continuous diffusion function. We present a novel approach, combining tangent process techniques, Feynman-Kac path integration, and exponential change of measures, to derive sharp exponential decays to equilibrium. We also provide uniform estimates with respect to the time horizon, quantifying with some precision the fluctuations of these diffusions around a limiting deterministic Riccati differential equation. These results provide a stronger and almost sure version of the conventional central limit theorem. We illustrate these results in the context of ensemble Kalman-Bucy filtering. In
砂漠の洪水を灌漑用水に変える --ヨルダンの乾燥地で数理的最適戦略によるプロトタイプを運用--. 京都大学プレスリリース. 2018-03-08.Operation of reservoirs is a fundamental issue in water resource management. We herein investigate well-posedness of an optimal control problem for irrigation water intake from a reservoir in an irrigation scheme, the water dynamics of which is modeled with stochastic differential equations. A prototype irrigation scheme is being developed in an arid region to harvest flash floods as a source of water. The Hamilton-Jacobi-Bellman (HJB) equation governing the value function is analyzed in the framework of viscosity solutions. The uniqueness of the value function, which is a viscosity solution to the HJB equation, is demonstrated with a mathematical proof of a comparison theorem. It is also shown that there exists such a viscosity solution. Then, an approximate value function is obtained as a numerical solution to the HJB ...
Title: Particle representations for SPDEs and strict positivity of solutions Abstract: Stochastic partial differential equations arise naturally as limits of finite systems of interacting particles. For a variety of purposes, it is useful to keep the particles in the limit obtaining an infinite exchangeable system of stochastic differential equations. The corresponding de Finetti measure then gives the solution of the SPDE. These representations frequently simplify existence, uniqueness and convergence results. The support properties of the measure-valued solution can be studied using Girsanov change of measure techniques. The ideas will be illustrated by a model of asset prices set by an infinite system of competing traders. These latter results are joint work with Dan Crisan and Yoonjung Lee. ...
This paper focuses on estimation of an non-integral quadratic function (NIQF) and integral quadratic function (IQF) of a random signal in dynamic system described by a linear stochastic differential equation. The quadratic form of an unobservable signal indicates useful information of a signal for control. The optimal (in mean square sense) and suboptimal estimates of NIQF and IQF represent a function of the Kalman estimate and its error covariance. The proposed estimation algorithms have a closed-form estimation procedure. The obtained estimates are studied in detail, including derivation of the exact formulas and differential equations for mean square errors. The results we demonstrate on practical example of a power of signal, and comparison analysis between optimal and suboptimal estimators is presented. ...
Abstract: Stochastic partial differential equations frequently arise as limits of finite systems of weighted interacting particles. For a variety of purposes, it is useful to keep the particles in the limit obtaining an infinite exchangeable system of stochastic differential equations for the particle locations and weights. The corresponding de Finetti measure then gives the solution of the SPDE. These representations frequently simplify existence, uniqueness and convergence results. Following some discussion of general approaches to SPDEs, the talk will focus on situations where the particle locations are given by an iid family of diffusion processes, and the weights are chosen to obtain a nonlinear driving term and to match given boundary conditions for the SPDE. (Recent results are joint work with Dan Crisan ...
The arbitrage-free Multivariate Mixture Dynamics Model: Consistent single-assets and index volatility smiles. Abstract: We introduce a multivariate diffusion model that is able to price derivative securities featuring multiple underlying assets. Each asset volatility smile is modeled according to a density-mixture dynamical model while the same property holds for the multivariate process of all assets, whose density is a mixture of multivariate basic densities. This allowsto reconcile single name and index/basket volatility smiles in a consistent framework. Our approach could be dubbed a multidimensional local volatility approach with vector-state dependent diffusion matrix. The model is quite tractable, leading to a complete market and not requiring Fourier techniques for calibration and dependence measures, contrary to multivariate stochastic volatility models such as Wishart. We prove existence and uniqueness of solutions for the model stochastic differential equations, provide formulas for a ...
Stochastic processes are probabilistic models for random quantities evolving in time or space. The evolution is governed by some dependence relationship between the random quantities at different times or locations. Major classes of stochastic processes are random walks, Markov processes, branching processes, renewal processes, martingales, and Brownian motion. Important application areas are mathematical finance, queuing processes, analysis of computer algorithms, economic time series, image analysis, social networks, and modeling biomedical phenomena. Stochastic process models are used extensively in operations research applications.. ...
Description: The course material changes with each occurrence of the course and may be taken for credit repeatedly with the instructors permission. Continuous time random processes, Kolmogorovs continuity theorem. Brownian Motion: the Donsker invariance principle, Holder continuity, quadratic variation. Continuous-time martingales and square integrable martingales. Markov processes and the strong Markov property. Properties of Brownian Motion: strong Markov property, Blumenthal zero-one law, Law of Iterated Logarithm. Stochastic integration with respect to continuous local martingales, Itos formula, Levys Characterization of Brownian motion, Girsanov transformation, Stochastic Differential Equations with Lipschitz Coefficients. Other topics in probability theory and stochastic processes at the choice of the instructor (e.g. connection to PDEs, local time, Skorokhods embedding theorem, zeros of the BM, empirical processes, concentration inequalities and applications in non-parametric ...
If stochastic simulation of minimal cell models intrigues you, see may be interested in: Carletti T, Filisetti A.The stochastic evolution of a protocell: the gillespie algorithm in a dynamically varying volume.Comput Math Methods Med. 2012;2012:423627. Lazzerini-Ospri L, Stano P, Luisi P, Marangoni R.Characterization of the emergent properties of a synthetic quasi-cellular system.BMC Bioinformatics. 2012 Mar 28;13 Suppl 4:S9. Zachar I, Fedor A, Szathmáry E.Two different template replicators coexisting in the same protocell: stochastic simulation of an extended chemoton model.PLoS One. 2011;6(7):e21380. Epub 2011 Jul 19. Van Segbroeck S, Nowé A, Lenaerts T.Stochastic simulation of the chemoton.Artif Life. 2009 Spring;15(2):213-26. | Origin of Life: Emergence, Self-organization and Evolution
This thesis consists of the four papers which consider different aspects of stochastic process modeling, error analysis, and minimization of computational cost.. In Paper I, we construct a Multipath Fading Channel (MFC) model for wireless channels with noise introduced through scatterers flipping on and off. By coarse graining the MFC model a Gaussian process channel model is developed. Complexity and accuracy comparisons of the models are conducted.. In Paper II, we generalize a multilevel Forward Euler Monte Carlo method introduced by Mike Giles for the approximation of expected values depending on solutions of Ito stochastic differential equations. Giles work proposed and analyzed a Forward Euler Multilevel Monte Carlo (MLMC) method based on realizations on a hierarchy of uniform time discretizations and a coarse graining based control variates idea to reduce the computational cost required by a standard single level Forward Euler Monte Carlo method. This work is an extension of Giles MLMC ...
Toxoplasma gondii (T. gondii) is an intracellular protozoan parasite. The parasite can infect all warm-blooded vertebrates. Up to 30% of the worlds human population carry a Toxoplasma infection. However, the transmission dynamics of T. gondii has not been well understood, although a lot of mathematical models have been built. In this thesis, we adopt a complex life cycle model developed by Turner et al. and extend their work to include diffusion of hosts. Most of researches focus on the deterministic models. However, some scientists have reported that deterministic models sometimes are inaccurate or even inapplicable to describe reaction-diffusion systems, such as gene expression. In this case stochastic models might have qualitatively different properties than its deterministic limit. Consequently, the transmission pathways of T. gondii and potential control mechanisms are investigated by both deterministic and stochastic model by us. A stochastic algorithm due to Gillespie, based on the ...
TY - JOUR. T1 - Environmental Brownian noise suppresses explosions in population dynamics. AU - Mao, Xuerong. AU - Marion, Glenn. AU - Renshaw, Eric. PY - 2002. Y1 - 2002. N2 - Population systems are often subject to environmental noise, and our aim is to show that (surprisingly) the presence of even a tiny amount can suppress a potential population explosion. To prove this intrinsically interesting result, we stochastically perturb the multivariate deterministic system ẋ(t) = f(x(t)) into the Itô form dx(t) = f(x(t))dt + g(x(t))dw(t), and show that although the solution to the original ordinary differential equation may explode to infinity in a finite time, with probability one that of the associated stochastic differential equation does not. AB - Population systems are often subject to environmental noise, and our aim is to show that (surprisingly) the presence of even a tiny amount can suppress a potential population explosion. To prove this intrinsically interesting result, we ...
Find all books from Wendell Fleming, P.L. Lions, Wendell Helms Fleming - Stochastic Differential Systems, Stochastic Control Theory, and Applications (The IMA Volumes in Mathematics and Its Applications). At find-more-books.com you can find used, antique and new books, COMPARE results and immediately PURCHASE your selection at the best price. 0387966412
We present single-cell clustering using bifurcation analysis (SCUBA), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying SCUBA to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between RT-PCR and RNA sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between SCUBA predictions and the experimental ...
In the recent years, the problems of stability of delayed neural networks have received much attention due to its potential application in associative memories, pattern recognition and optimization. A large number of results have appeared in literature, see, for example, [1-14]. As is well known, a real system is usually affected by external perturbations which in many cases are of great uncertainty and hence may be treated as random [15-17]. As pointed out by Haykin [18] that in real nervous systems synaptic transmission is a noisy process brought on by random fluctuations from the release of neurotransmitters and other probabilistic causes, it is of significant importance to consider stochastic effects for neural networks. In recent years, the dynamic behavior of stochastic neural networks, especially the stability of stochastic neural networks, has become a hot study topic. Many interesting results on stochastic effects to the stability of delayed neural networks have been reported (see ...
The principle themes are to characterize the time evolution of the scattered field in terms of stochastic differential equations, and to illustrate this framework in simulation and experimental data analysis. The physical models contain all correlation information and higher order statistics, which enable radar and laser scattering experiments to be interpreted. An emphasis is placed on the statistical character of the instantaneous fluctuations, as opposed to ensemble average properties. This leads to various means for detection, which have important consequences in radar signal processing and statistical optics. There are also significant connections with ideas in mathematical finance that can be applied to physics problems in which non-Gaussian noise processes play an essential role.. ...
P: M311 and M365. R: M343. Course covers probability theory, Brownian motion, Itos Lemma, stochastic differential equations, and dynamic hedging. These topics are applied to the Black-Scholes formula, the pricing of financial derivatives, and the term theory of interest rates ...
I am a statistician and computational biologist working in the field of BigData analytics (high-throughput data analysis). I received my PhD in Mathematics (area: stochastic differential equations) from the University of Rochester in 2004. I joined the faculty of the Department of Biostatistics and Computational Biology at the University of Rochester in 2007.. I have developed many statistical and computational methods for analyzing and integrating large-scale data with complex correlation structures such as various Omics data (gene expression, protein expression, microbiota abundance and diversity, etc) and medical image data (primarily structural MRI data).. I have published 70 journal articles and book chapters, covering research topics such as Omics data pre-processing, hypothesis testing, multiple testing adjustment, functional data analysis, cluster analysis, network analysis, gene set enrichment analyses, and spatial statistical analysis for Diffusion Tensor Imaging data.. Examples of my ...
The Research Office is pleased to announce the following proposals have been selected for funding for the Faculty Release Time (FRT) 2010-11 Winter (Spring release) solicitation.. • Boudraa, Nabil (School of Language, Society and Culture, College of Liberal Arts): Writing the Natural World in French and Francophone Literatures. • Gibson, Nathan (Dept. of Mathematics, College of Science): Numerical Methods for Random and Stochastic Differential Equations with Applications to Mathematical Biology. • Kingston, Deanna (Dept. of Anthropology, College of Arts and Sciences): Iñupiaq Ecological Knowledges and Geographic Information Sciences. The Research Office is pleased to announce the awards for the Undergraduate Research, Innovation, Scholarship and Creativity (URISC) 2010-11 Winter/Spring solicitation.. The following proposals have been selected for funding:. • McClanahan, Danielle [Major: Bioengineering] (Faculty Project Advisor: Adam Higgins, School of Chemical, Biological and ...
Recent years have seen an explosion of interest in stochastic partial differential equations where the driving noise is discontinuous. In this comprehensive monograph, two leading experts detail the evolution equation approach to their solution. Most of the results appeared here for the first time in book form. The authors start with a detailed analysis of Lévy processes in infinite dimensions and their reproducing kernel Hilbert spaces; cylindrical Lévy processes are constructed in terms of Poisson random measures; stochastic integrals are introduced. Stochastic parabolic and hyperbolic equations on domains of arbitrary dimensions are studied, and applications to statistical and fluid mechanics and to finance are also investigated. Ideal for researchers and graduate students in stochastic processes and partial differential equations, this self-contained text will also interest those working on stochastic modeling in finance, statistical physics and environmental science. ...
Individuals change their behavior during an epidemic in response to whether they and/or those they interact with are healthy or sick. Healthy individuals may utilize protective measures to avoid contracting a disease. Sick individuals may utilize preemptive measures to avoid spreading a disease. Yet, in practice both protective and preemptive changes in behavior come with costs. This paper proposes a stochastic network disease game model that captures the self-interests of individuals during the spread of a susceptible-infected-susceptible disease. In this model, individuals strategically modify their behavior based on current disease conditions. These reactions influence disease spread. We show that there is a critical level of concern, i.e., empathy, by the sick individuals above which disease is eradicated rapidly. Furthermore, we find that risk averse behavior by the healthy individuals cannot eradicate the disease without the preemptive measures of the sick individuals. Empathy is more ...
TY - CHAP. T1 - Generalized Statistical Thermodynamics. T2 - Thermodynamics of Probability Distributions and Stochastic Processes. AU - Matsoukas, Themis. PY - 2018/1/1. Y1 - 2018/1/1. UR - http://www.scopus.com/inward/record.url?scp=85065840046&partnerID=8YFLogxK. UR - http://www.scopus.com/inward/citedby.url?scp=85065840046&partnerID=8YFLogxK. M3 - Chapter. AN - SCOPUS:85065840046. T3 - Understanding Complex Systems. SP - 1. EP - 363. BT - Understanding Complex Systems. PB - Springer Verlag. ER - ...
TY - JOUR. T1 - Visual representations are dominated by intrinsic fluctuations correlated between areas. AU - Henriksson, Linda. AU - Khaligh-Razavi, Seyed Mahdi. AU - Kay, Kendrick. AU - Kriegeskorte, Nikolaus. N1 - Funding Information: This work was supported by the Aalto University , the European Research Council (Advanced Grant # 232946 to R. Hari) and the Academy of Finland Postdoctoral Researcher Grant ( 278957 ) to LH, and a European Research Council Starting Grant ( 261352 ) to NK. The authors declare no competing financial interests. Publisher Copyright: © 2015.. PY - 2015/7/1. Y1 - 2015/7/1. N2 - Intrinsic cortical dynamics are thought to underlie trial-to-trial variability of visually evoked responses in animal models. Understanding their function in the context of sensory processing and representation is a major current challenge. Here we report that intrinsic cortical dynamics strongly affect the representational geometry of a brain region, as reflected in response-pattern ...
books.google.comhttps://books.google.com/books/about/Limit_Theorems_for_Stochastic_Processes.html?id=sUgXKpUIdHwC&utm_source=gb-gplus-shareLimit Theorems for Stochastic Processes ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. One of the adverse effects of climate change is the proliferation of heat waves. Our investigations show that according to the most widely accepted climate change scenarios heat waves are expected to be essentially longer and hotter than in the past. It might happen that events we now define as heat waves last through entire summer. Although it will not be general, the length and intensity of present heat waves could also multiply. Based on data provided by some global circulation models, we might be face an event that exceeds the hottest heat waves of the 20th century by as much as 12°C. This study also offers a survey of the methodology of heat wave definition. Besides traditional calculations, we present two unconventional methods by introducing minimum and maximum temperature heat waves. We show in what points this approach is different from those usually adopted and what extra information it may offer. As an
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In this paper we study the geometrization of certain spaces of stochastic processes. Our main motivation comes from the problem of pattern recognition in high-dimensional time-series data (e.g.,...
The Young European Queueing Theorists (YEQT) workshops are organized on a yearly basis, and this year the 13th edition of the workshop will take place in October 2019. The aim of these workshops is to bring together young researchers, PhD students or recently appointed lecturers and assistant professors, and world-leading experts in order to share and discuss research related to queueing theory, operations research, applied probability and related areas. This event provides an excellent opportunity for developing researchers to interact and exchange ideas in an informal, friendly, yet research-focused setting. The workshop program will consist of presentations from young researchers and several keynote presentations and tutorials by prominent researchers.. The theme for the YEQT workshop this year is Data-Driven Analytics and Optimization for Stochastic Systems. The workshop will focus on combining theoretical stochastic modelling and optimization together with modern statistical techniques in ...
Appendix B A Few Math Facts This text assumes that the reader knows a variety of mathematical facts. Often these facts go unstated. For example, we use many properties of ... - Selection from Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 3rd Edition [Book]
Abstract. Stochastic processes have wide relevance in mathematics both for theoretical aspects and for their numerous real-world applications in various domains. They represent a very active research field which is attracting the growing interest of scientists from a range of disciplines.This Special Issue aims to present a collection of current contributions concerning various topics related to stochastic processes and their applications. In particular, the focus here is on applications of stochastic processes as models of dynamic phenomena in research areas certain to be of interest, such as economics, statistical physics, queuing theory, biology, theoretical neurobiology, and reliability theory. Various contributions dealing with theoretical issues on stochastic processes are also included ...
Abstract. Stochastic processes have wide relevance in mathematics both for theoretical aspects and for their numerous real-world applications in various domains. They represent a very active research field which is attracting the growing interest of scientists from a range of disciplines.This Special Issue aims to present a collection of current contributions concerning various topics related to stochastic processes and their applications. In particular, the focus here is on applications of stochastic processes as models of dynamic phenomena in research areas certain to be of interest, such as economics, statistical physics, queuing theory, biology, theoretical neurobiology, and reliability theory. Various contributions dealing with theoretical issues on stochastic processes are also included ...
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Premier atelier de lERC « Reaction-Diffusion Equations, Propagations and Modelling » Journées détude organisées par Henri Berestycki et Jean-Michel Roquejoffre EHESS, 24-25 septembre 2013 Séquence 1: Hiroshi Matano (University of Tokyo) Spreading speed for some two-component reaction-diffusion system In this talk I will discuss the spreading properties of solutions of a prey-predator type reaction-diffusion system. This system belongs to the class of reaction-diffusion systems for which the comparison principle does not hold. For such class of systems, little has been know about the spreading properties of the solutions. Here, by a spreading property, we mean the way the solution propagates when starting from compactly supported initial data. We show that propagation of both the prey and the predator occur with a definite spreading speed. Furthermore, quite intriguingly, the spreading speed of the prey and that of the predator are different in some
Radiation measurement units have undergone a change from Rads and Rems to Grays and Sieverts. However, radiobiology literature uses both systems, resulting in confusion for Radiologists ... The biological effects of radiation had been studied and documented within few years of the discovery of Xrays and further information has consequently been available from longitudinal studies on populations affected by the atomic bomb. Biological effects are classified as deterministic (or certainty effects) and stochastic effects. Both deterministic and stochastic effects may either result in changes in organs (somatic effects) or in the genes (genetic effects) ... Stochastic effects are random events which are not dose related but their probability increases with an increase in the radiation dose ... The Committee For Nuclear Responsibility (CNR) ...
Biochemical reactions are subject to stochastic fluctuations that can give rise to cell-to-cell variability. Yet, how this variability affects viral infections, which themselves involve noisy reactions, remains largely elusive. Here we present single-cell experiments and stochastic simulations that …
There is no firm basis for setting a safe level of exposure above background for stochastic effects. Many sources emit radiation that is well below natural background levels. This makes it extremely difficult to isolate its stochastic effects. In setting limits, EPA makes the conservative (cautious) assumption that any increase in radiation exposure is accompanied by an increased risk of stochastic effects. Health physicists generally agree on limiting a persons exposure beyond background radiation to about 100 mrem per year from all sources. Exceptions are occupational, medical or accidental exposures. (Medical X-rays generally deliver less than 10 mrem). However, there do appear to be threshold exposures for the various non-stochastic effects. (Please note that the acute affects in the following table are cumulative. For example, a dose that produces damage to bone marrow will have produced changes in blood chemistry and be accompanied by nausea ...
They are simply a self-fulfilling prophecy. You get back what you built into the model.. The point of models like these are not to prove something by themselves. Instead, they allow you to identify simpler propositions to prove, which, if true, would establish that the model is right.. For example, if the predominant way that an independent chiefdomship arises without a war is as a result of the death of chief, then we know that this kind of event will have big implications for the dynamics (a non-obvious result). Then, we can dig through the best available datat in chiefdomship societies that are documented historically to determine what historically did cause independent chiefdomships to arise. If our model assumption is supported, we get a lot more bang for our buck out of that factual discovery than we would otherwise.. Similar garbage in, garbage out criticisms can be made of a recent contagion model of terrorism, where the key variable is the extent to which a harsh suppression of a ...
TY - JOUR AU - Jakšić, Olga AU - Jakšić, Zoran AU - Čupić, Željko AU - Ranđelović, Danijela AU - Kolar-Anić, Ljiljana PY - 2014 UR - http://cer.ihtm.bg.ac.rs/handle/123456789/1497 AB - The basic parameters of a sensor element defining its ultimate performance are sensitivity and intrinsic noise. In plasmonic gas sensors both are determined by refractive index changes due to adsorption and desorption (a-d) of target analyte particles to the sensor active area. In this paper we present a general model that can be simultaneously used to determine sensitivity and intrinsic noise of a plasmonic sensor both during transients and in steady-state and is valid for multi-analyte environments. The model utilizes the conventional probabilistic approach. It is derived without any assumptions about the stochastic nature of the fundamental (a-d) process. It reveals how all stochastic properties of the processes with (pseudo) first order kinetics with the initial number of particles equal to zero can ...
Chemical kinetics, also called reaction kinetics, is studying how fast chemical reactions go. This includes studying how different conditions such as temperature, pressure or solvent used affect the speed of a reaction. Chemical kinetics can also be used to find out about reaction mechanisms and transition states. The basic idea of chemical kinetics is called collision theory. This states that for a reaction to happen, the molecules must hit each other. Ways of increasing the speed of the reaction must therefore increase the number of hits. This can be done in many ways. With experiments it is possible to calculate reaction rates from which you can get rate laws and rate constants. A rate law is a mathematical expression with which you can calculate the speed of a reaction given the concentration of the reagents. ...
Mathematical Problems in Engineering is a peer-reviewed, Open Access journal that publishes results of rigorous engineering research carried out using mathematical tools. Contributions containing formulations or results related to applications are also encouraged. The primary aim of Mathematical Problems in Engineering is rapid publication and dissemination of important mathematical work which has relevance to engineering. All areas of engineering are within the scope of the journal. In particular, aerospace engineering, bioengineering, chemical engineering, computer engineering, electrical engineering, industrial engineering and manufacturing systems, and mechanical engineering are of interest. Mathematical work of interest includes, but is not limited to, ordinary and partial differential equations, stochastic processes, calculus of variations, and nonlinear analysis.
A Langevin canonical framework for a chiral two-level system coupled to a bath of harmonic oscillators is used within a coupling scheme different from the well-known spin-boson model to study the quantum stochastic resonance for chiral molecules. This process refers to the amplification of the response to an external periodic signal at a certain value of the noise strength, being a cooperative effect of friction, noise, and periodic driving occurring in a bistable system. Furthermore, from this stochastic dynamics within the Markovian regime and Ohmic friction, the competing process between tunneling and the parity violating energy difference present in this type of chiral systems plays a fundamental role. This mechanism is finally proposed to observe the so-far elusive parity-violating energy difference in chiral molecules.
With reference to spacecraft communication, the paper examines the effect of radio wave scattering at a statistically rough surface (the midsurface coincides with a sphere) on the characteristics of a radio channel. The statistical characteristics of the orthogonal components of the radio signal at the receiver are calculated with consideration of shading. Amplitude and phase distributions of the sum signal are determined under the assumption of a Gaussian distribution of the quadrature components of the scattered signal.
The stochastic resonance (SR) phenomenon induced by a multiplicative periodic signal in a logistic growth model with correlated noises is studied by using the theory of signal-to-noise ratio (SNR) in the adiabatic limit. The expressions of the SNR are obtained. The effects of multiplicative noise intensity α and additive noise intensity D, and correlated intensity λ on the SNR are discussed respectively. It is found that the existence of a maximum in the SNR is the identifying characteristic of the SR phenomena. In comparison with the SR induced by additive periodic signal, some new features are found: (1) When SNR as a function of λ for fixed ratio of α and D, the varying of α can induce a stochastic multi-resonance, and can induce a re-entrant transition of the peaks in SNR vs λ; (2) There exhibits a doubly critical phenomenon for SNR vs D and λ, i.e., the increasing of D (or λ) can induce the critical phenomenon for SNR with respect to λ (or D); (3) The doubly stochastic resonance ...
RICHMOND, Va. - Ed Gillespies perceived snubbing of a former Donald Trump operative he had hired to whip up support for his gubernatorial campaign is causing an uproar among GOP activists already exasperated by Gillespies highly cautious stance toward the president.. Jack Morgan had expected to play a key role at a Gillespie rally headlined by Vice President Mike Pence on Saturday in Abingdon, a coal country region that voted overwhelmingly for Trump in November, activists said. But he found himself sidelined.. Gillespie hired Morgan, a colorful evangelical preacher and former 9th Congressional District GOP chairman, as his ambassador to Trump country after nearly losing the June primary to a rival who had run in the presidents bombastic, populist image.. But Gillespies campaign did not let Morgan help plan or speak at the rally - over the objections of another GOP candidate who employs Morgan, state Sen. Jill Holtzman Vogel, who is running for lieutenant governor and wanted him to introduce ...
Relationships between Models of Genetic Regulatory Networks with Emphasis on Discrete State Stochastic Models: 10.4018/978-1-5225-0353-8.ch002: Genetic Regulatory Networks (GRNs) represent the interconnections between genomic entities that govern the regulation of gene expression. GRNs have been
Emergence of peakons and anti-peakons for the solution of the Camassa-Holm equation with stochastic transport (Dr Igor Shevchenko, Imperial College London). ...
Goodness of Fit in Nonlinear Dynamics: Mis-specified Rates or Mis-specified States?. Giles Hooker, Stephen P. Ellner(Submitted on 2 Dec 2013). This paper introduces tests to uncover the nature of lack of fit in ordinary differential equation models (ODEs) proposed for data. We present a hierarchy of three possible sources of lack of fit: unaccounted-for stochastic variation, mis-specification of functional forms in the rate equations, and missing dynamical variables in the description of the system. We represent lack of fit by allowing some parameters to vary over time, and propose generic testing procedures that do not rely on specific alternative models. Our hypotheses are expressed in terms of nonparametric relationships among latent variables, and the tests are carried out through a combined residual bootstrap and permutation methods. We demonstrate the effectiveness of these tests on simulated data, and on real data from laboratory ecological experiments and electro-cardiogram ...
The workshop takes place in the Erhard-Schmidt lecture room at Weierstrass Institute, Mohrenstrasse 39, 10117 Berlin, Germany. The lecture room is equipped with a blackboard and a digital projector as well as intranet and internet access. Information for Visitors: Berlin city map view. ...
Bob Costantino and Brett Melbourne are joining us for the next two days to talk about stochastic population dynamics in Tribolium. ...
1. Suppose that shocks occur according to a Poisson process with rate A| 0. Also suppose that each shock independently causes the system to fail with probability 0 | p | 1. Let N denote the number of shocks that it takes for the.
Probability theory, mathematical statistics, stochastic processes, random evolutions, diffusion processes at finite speed, transport processes in biological and physical systems, random walks, stochastic processes in random environments, partial differential equations in stochastic models, statistical physics, wave processes, probabilities on algebraic structures, statistical quality control ...
To get a better understanding of the dynamics of the system, we made a model of the system using the Cain software[1] for stochastic simulations. While stochastic simulations are more computationally demanding than deterministic models based on solving ODEs, they allow for random fluctuations that may have a big impact on the system in a cell[2]. As we want our system to react to two different signals, three promoters were required. One to respond to lactate, one to respond to low oxygen and a third to respond to signal molecules controlled by the two other promoters. The last promoter would then control cell lysis. For lactate sensing, we have adapted the lld promoter of E. coli, the vgb promoter from Vitreoscilla is used for the oxygen while the lux promoter from Vibrio fischeri was used for lysis control. The model can be divided into three parts; the lld promoter, the vgb promoter and the lux promoter. Each of these systems was first modelled separately. This made it easier to observe the ...
Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GRN dynamics are usually investigated via stochastic models since molecular interactions are basically discrete and stochastic processes. However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. ...
If you have a question about this talk, please contact Thomas Lippincott.. In recent years computational linguists, psycholinguists, and even some theoretical linguists have adoped a probabilistic view of linguistic knowledge. The primary motivation for this approach is a concern to incorporate the gradient effects and soft, defeasible constraints evident in speakers variable judgements on acceptability into the theory of linguistic competence. On this view knowledge of a language is identified directly with a language model and the probability distribution over the strings of a language that it specifies. I will take up some of the problems involved in developing a viable stochastic representation of competence and suggest possible solutions to these problems. I will also look at the connections between probabilistic theories of learning and a stochastic model of grammar. Finally, I will consider several consequences that such a model has for the competence-performance distinction.. This talk ...
The Senate of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) has announced the establishment of a new Priority Programme, entitled Probabilistic Structures in Evolution (SPP 1590). The programme is designed to run for six years. Biological evolution is a complex phenomenon driven by various underlying processes, such as mutation and recombination of genetic material, reproduction of individuals, competition, and selection of favourable types. Studying the interplay of these processes requires a substantial use of mathematical models and methods. Over the past decades, much of this modelling and analysis took place on a deterministic level, using dynamical systems and differential equations, and this has led to an elaborate theory. However, the processes of evolution have intrinsically random elements, such as random reproduction, which leads to stochastic fluctuations of gene frequencies and the emergence of random genealogies. The underlying stochastic processes are ...
Doob decomposition theorem Doob 1953 Meyer 1952 Meyer 1963 Protter 2005 Protter (2005) Doob, J. L. (1953). Stochastic Processes ... Protter, Philip (2005). Stochastic Integration and Differential Equations. Springer-Verlag. pp. 107-113. ISBN 3-540-00313-4.. ... Let Z {\displaystyle Z} be a cadlag submartingale of class D. Then there exists a unique, increasing, predictable process A {\ ... The Doob-Meyer decomposition theorem is a theorem in stochastic calculus stating the conditions under which a submartingale may ...
Stochastic Processes. New Age International. ISBN 9788122405491 - via Google Books. "STOCHASTIC MODELS IN QUEUEING THEORY" (PDF ... He also published two books on stochastic processes each with over 500 citations. The J Medhi memorial lecture is annually held ...
Books - (1953). Stochastic Processes. John Wiley & Sons. ISBN 0-471-52369-0. - (1984). Classical Potential Theory and Its ... 1975). "Stochastic process measurability conditions" (PDF). Annales de l'Institut Fourier. 25 (3-4): 163-176. doi:10.5802/aif. ... After writing a series of papers on the foundations of probability and stochastic processes including martingales, Markov ... "Review of Stochastic processes by J. L. Doob". Bull. Amer. Math. Soc. 60: 190-201. doi:10.1090/S0002-9904-1954-09801-4. Meyer, ...
Doob, J. L. (1953). Stochastic processes. 101. New York: Wiley. p. 293.. ... is a mathematical construction of a stochastic process which approximates a given random variable and has the martingale ... process { Z 0 , Z 1 , … } {\displaystyle \left\{Z_{0},Z_{1},\dots \right\}} where Z 0 := E [ f ( X 1 , X 2 , … , X n ) ∣ F 0 ...
... and the Poisson process, which are considered the most important and central stochastic processes in the theory of stochastic ... A Markov process is a stochastic process that satisfies the Markov property (sometimes characterized as "memorylessness"). In ... Two important examples of Markov processes are the Wiener process, also known as the Brownian motion process, ... Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo, which are used for ...
... stochastic control; estimation theory; stochastic processes; and mathematical economics. Tamer Başar at the Mathematics ... CS1 maint: discouraged parameter (link) For seminal contributions to dynamic games, stochastic and risk-sensitive control, ...
Many phenomena investigated by physicists are not static but evolve probabilistically with time (i.e. Stochastic process). The ... Hassan, M.K.; Pavel, N.I.; Pandit, R.K.; Kurths, J. (2014). "Dyadic Cantor set and its kinetic and stochastic counterpart". ... the area size distribution of the blocks of weighted planar stochastic lattice (WPSL) also exhibits dynamic scaling.[citation ... Zahedul; Islam, Nabila (2013-10-24). "Emergence of fractals in aggregation with stochastic self-replication". Physical Review E ...
"Kac's moment formula and the Feynman-Kac formula for additive functionals of a Markov process". Stochastic Processes and Their ... Oelschläger, Karl (1984). "A martingale approach to the law of large numbers for weakly interacting stochastic processes". Ann ... Del Moral, Pierre; Miclo, Laurent (2000). "A Moran particle system approximation of Feynman-Kac formulae". Stochastic Processes ... Malrieu, Florent (2001). "Logarithmic Sobolev inequalities for some nonlinear PDE's". Stochastic Process. Appl. 95 (1): 109-132 ...
ISBN 0-387-22833-0. Bass, Richard F. (2011). Stochastic Processes. Cambridge: Cambridge University Press. pp. 356-357. ISBN 978 ...
Barbour, A. D. & Brown, T. C. (1992). "Stein's method and point process approximation". Stochastic Processes and Their ... such as Gaussian processes by Barbour (1990), the binomial distribution by Ehm (1991), Poisson processes by Barbour and Brown ( ... If A {\displaystyle {\mathcal {A}}} is the generator of a Markov process ( Z t ) t ≥ 0 {\displaystyle (Z_{t})_{t\geq 0}} (see ... ISBN 978-1-43983-574-6. Barbour, A. D. (1988). "Stein's method and Poisson process convergence". Journal of Applied Probability ...
doi:10.1090/s0002-9904-1953-09662-8. J. L. Doob (1953). Stochastic Processes. John Wiley & Sons. ISBN 0-471-52369-0. Olav ... Probability and Random Processes (3rd ed.). Oxford University Press. ISBN 0-19-857222-0., pages 67-69 Ushakov, N.G. (2001) [ ...
Advanced Stochastic Processes, Fall 2013, 10/9/2013. Bobrowski, Adam (2005). Functional Analysis for Probability and Stochastic ... In mathematics - specifically, in the theory of stochastic processes - Doob's martingale convergence theorems are a collection ... Stochastic Processes. New York: Wiley. Durrett, Rick (1996). Probability: theory and examples (Second ed.). Duxbury Press. ISBN ... Øksendal, Bernt K. (2003). Stochastic Differential Equations: An Introduction with Applications (Sixth ed.). Berlin: Springer. ...
ISBN 978-0-521-66349-6.. Brzezniak, Zdzislaw; Zastawniak, Thomasz (2000). Basic Stochastic Processes. Springer. ISBN 3-540- ...
In mathematics, finite-dimensional distributions are a tool in the study of measures and stochastic processes. A lot of ... be a stochastic process. The finite-dimensional distributions of X {\displaystyle X} are the push forward measures P i 1 … i k ... The definition of the finite-dimensional distributions of a process X {\displaystyle X} is related to the definition for a ... information can be gained by studying the "projection" of a measure (or process) onto a finite-dimensional vector space (or ...
Controlled stochastic processes, Springer Verlag 1979 with A. V. Skorokhod: The Theory of Stochastic Processes, Springer Verlag ... The Theory of Stochastic Processes III. Springer-Verlag. Stroock, Daniel W. (1980). "Review: The theory of stochastic processes ... Gikhman is well-known for a comprehensive treatise on the theory of stochastic processes, co-authored with Skorokhod. In the ... Iosif Ilyich Gikhman; Anatoly Vladimirovich Skorokhod (2004). The Theory of Stochastic Processes II. Springer-Verlag. Iosif ...
ISBN 978-0-444-52965-7. Erhan Cinlar (1975). Introduction to Stochastic Processes. Prentice Hall Inc, New Jesry. ISBN 978-0-486 ... Markov process Continuous-time Markov process Master equation Detailed balance Graph theory Semi-Markov process van Kampen, N. ... These terms are different than a birth-death process, where there is simply a linear kinetic scheme. A birth-death process is a ... Usually a kinetic scheme represents a Markovian process, while for non-Markovian processes generalized kinetic schemes are used ...
Andrushkiw RI; Klyushin DD; Petunin YI (2008). "A new test for unimodality". Theory of Stochastic Processes. 14 (1): 1-6. ... Freeman; Dale (2012). "Assessing bimodality to detect the presence of a dual cognitive process" (PDF). Behavior Research ...
A stochastic two-state trajectory is among the simplest stochastic processes. Extensions include: three-state trajectories, ... Introduction to Stochastic Processes. Prentice Hall Inc, New Jersey. ISBN 978-0-486-49797-6. Moerner, W. E.; Orrit, M (1999). " ... Colquhoun, D.; Hawkes, A. G. (1982). "On the Stochastic Properties of Bursts of Single Ion Channel Openings and of Clusters of ... Fredkin, Donald R.; Rice, John A. (1986). "On Aggregated Markov Processes". Journal of Applied Probability. 23 (1): 208-14. doi ...
Jazwinski, Andrew H. (1970). Stochastic Processes and Filtering. Mathematics in Science and Engineering. New York: Academic ... This process essentially linearizes the non-linear function around the current estimate. See the Kalman Filter article for ... Signal Process. 51 (9): 2288-2293. Bibcode:2003ITSP...51.2288E. doi:10.1109/tsp.2003.815376. hdl:2440/2403. Einicke, G.A.; ... Nonlinear Statistical Signal Processing Workshop, 2006 IEEE. pp. 201-202. doi:10.1109/NSSPW.2006.4378854. ISBN 978-1-4244-0579- ...
Some properties of measurable stochastic processes. Ambrose, Warren (1940). "On measurable stochastic processes". Transactions ...
Pollard, D. (1984). Convergence of Stochastic Processes. Springer. ISBN 9781461252542. Anthony, Martin; Bartlett, Peter L. ( ...
Paco A Lagestrom (1953). Notes on Stochastic processes. "Notes on Stochastic processes" (PDF). Paco A Lagestrom, Julian D. Cole ...
ISBN 978-0-387-71939-9. Bhattacharya, Rabi N.; Waymire, Edward C. (2009). Stochastic Processes with Applications. SIAM. ... He has also contributed significantly to the theory and application of Markov processes, including numerous co-authored papers ...
Knill, O. (2006). Probability Theory & Stochastic Processes. India: Overseas Press. Mattilla, P. (1995). Geometry of Sets in ...
The process continues forever, indexed by the natural numbers. An example of a stochastic process which is not a Markov chain ... Other stochastic processes can satisfy the Markov property, the property that past behavior does not affect the process, only ... In probability, a discrete-time Markov chain (DTMC) is a sequence of random variables, known as a stochastic process, in which ... ISBN 978-1-119-38755-8. Richard Durrett (19 May 2012). Essentials of Stochastic Processes. Springer Science & Business Media. p ...
... (born 1944) is a French mathematician specializing in Stochastic processes and probability theory. He has been a ... Jacod, Jean; Shiryaev, Albert N. (1987). Limit theorems for stochastic processes. doi:10.1007/978-3-662-05265-5. ISBN ... Malliavin calculus and statistics of stochastic processes. Jean Jacod graduated from Ecole Polytechnique in 1965 and obtained ... Appl., 118, 517-559 (2008). Y. AIT-SAHALIA, J. JACOD: Testing for jumps in a discretely observed process. Annals of Statistics ...
"Convergence towards Burger's equation and propagation of chaos for weakly asymmetric exclusion processes". Stochastic Processes ... From 1987 to 1989 Gärtner and Dawson wrote a series of important papers on the McKean-Vlasov process. Their results were ... Dawson, D.A.; Gärtner, J. (1988). "Long-time behaviour of interacting diffusions". In J.R. Norris (ed.). Stochastic Calculus in ... Gärtner, Jürgen; König, Wolfgang (2005). "The Parabolic Anderson Model". Interacting Stochastic Systems. pp. 153-179. doi: ...
ISBN 0-691-14062-6. Ibe, Oliver C. (2009). Markov processes for stochastic modeling. Academic Press. p. 98. ISBN 0-12-374451-2 ... by approximating the process by a discrete time Markov chain. The original chain is scaled by the fastest transition rate γ, so ... "The Randomization Technique as a Modeling Tool and Solution Procedure for Transient Markov Processes". Operations Research. 32 ... described by a discrete Markov chain with transition matrix P as defined above where jumps occur according to a Poisson process ...
Fourier Analysis of Stochastic Processes. Springer, 2014. A. Baddeley. A crash course in stochastic geometry. Stochastic ... which is an example of a stationary stochastic process. Campbell's theorem for general point processes gives a method for ... For general point processes, Campbell's theorem is only for sums of functions of a single point of the point process. To ... Baddeley, A.; Barany, I.; Schneider, R.; Weil, W. (2007). "Spatial Point Processes and their Applications". Stochastic Geometry ...
Stochastic Processes and Their Applications. 117 (11): 1724-1749. doi:10.1016/j.spa.2007.01.013. Bannier, Christina E.; Hirsch ...
Sensory processing[edit]. Early models of sensory processing understood within a theoretical framework are credited to Horace ... Unstable synapses are easy to train but also prone to stochastic disruption. Stable synapses forget less easily, but they are ... Koch, Christof (1999). Biophysics of computation: information processing in single neurons. Oxford [Oxfordshire]: Oxford ... Neural Information Processing Systems (NIPS)- a leading annual conference covering other machine learning topics as well. ...
Individuals commonly engage in behavioral assimilation, a process in which they tend to match their own behaviors to those ... Stochastic game. *n-player game. *Large Poisson game. *Nontransitive game. *Global game ...
For the stochastic distribution, see Rayleigh distribution. For the wireless communication effect, see Rayleigh fading. ... Parametric Process. *Bragg's law. Works[edit]. *. Strutt, J.W (1871). "XV. On the light from the sky, its polarization and ...
A common application of the law is the analytic hierarchy process. Further progress was made by Georg Rasch (1960), who ...
Distinguished for his work on molecular evolution, in particular on the role of stochastic events in determining the rate of ... and of his contribution to understanding how genetic processes specify spatial information. ...
Each process activated by the proteins of the cell cycle engine involve a cascade of many reactions. The longest subsystem ... been exquisitely optimized by evolutionary selection as a total system for robust operation in the face of internal stochastic ...
If one is only interested in stochastic ordering of the two populations (i.e., the concordance probability P(Y,X)), the Mann- ... Animal Behavior Processes. 2: 285-302. doi:10.1037/0097-7403.2.4.285.. ... including a stochastic ordering (where the cumulative distribution functions satisfied the pointwise inequality FX(t) , FY(t) ...
is accepted, and the process continues until no change can be found to improve the value of f. (. x. ). {\displaystyle f(\ ... Stochastic hill climbing does not examine all neighbors before deciding how to move. Rather, it selects a neighbor at random, ... Other local search algorithms try to overcome this problem such as stochastic hill climbing, random walks and simulated ... or on memory-less stochastic modifications (like simulated annealing). ...
Iterated Stochastic Measurements (Technical report). arXiv:1210.0425 . Bibcode:2012JPhA...45W4020B. doi:10.1088/1751-8113/45/49 ... This misconception is rooted in a poor understanding of the quantum wave function ψ and the quantum measurement process.[3][4][ ... The observer has, rather, only the function of registering decisions, i.e., processes in space and time, and it does not matter ... The dynamics of a quantum system under continuous observation are described by a quantum stochastic master equation known as ...
The selection process is deterministic, although it may be based on earlier preferences established by the same process. ... Modern science, on the other hand, is a mixture of deterministic and stochastic theories.[154] Quantum mechanics predicts ... but is inferred from various cues through an intricate mental process, authorship processing. Although many interpret this work ... Deliberative indeterminism asserts that the indeterminism is confined to an earlier stage in the decision process.[61][62] This ...
Offline data processing is produced both in Kamioka and United States. In Kamioka[edit]. The offline data processing system is ... Stochastic electrodynamics. *Eigenstate thermalization hypothesis. *Yang-Mills theory. *N = 4 supersymmetric Yang-Mills theory ... Slow control monitor and offline process monitor[edit]. There is a process called the "slow control" monitor, as part of the ... A system dedicated to offsite offline data processing was set up at the Stony Brook University in Stony Brook, NY to process ...
Stochastic diffusion search (Bishop 1989)[edit]. Main article: Stochastic diffusion search. First published in 1989 Stochastic ... Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a ... Bishop, J.M., Stochastic Searching Networks, Proc. 1st IEE Int. Conf. on Artificial Neural Networks, pp. 329-331, London, UK, ( ... Swarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties such as found in art and ...
Native species can be threatened with extinction[113] through the process of genetic pollution. Genetic pollution is ... "Niche tradeoffs, neutrality, and community structure: A stochastic theory of resource competition, invasion, and community ... Levine, J. M. (2000). "Species diversity and biological invasions: Relating local process to community pattern". Science. 288 ( ... or processed into pet foods, or mink feed. Water hyacinth can be turned into fuel by methane digesters,[73] and other invasive ...
Climate is sometimes modeled as a stochastic process but this is generally accepted as an approximation to processes that are ... Climate research is made difficult by the large scale, long time periods, and complex processes which govern climate. Climate ...
For two stochastic processesEdit. Independence of two stochastic processes is a property between two stochastic processes {. X ... Notice that independence of a stochastic process is a property within a stochastic process, not between two stochastic ... For stochastic processesEdit. For one stochastic processEdit. The definition of independence may be extended from random ... Formally, two stochastic processes {. X. t. }. t. ∈. T. {\displaystyle \left\{X_{t}\right\}_{t\in {\mathcal {T}}}}. and {. Y. t ...
... characterized the formal properties of robust functions that have an equivalent line-process form and provided a process to ... "Stochastic tracking of 3D human figures using 2D image motion". European Conference on Computer Vision (ECCV). ECCV. Dublin, ... "IEEE Transactions on Image Processing. 7 (3): 421-432. Bibcode:1998ITIP....7..421B. doi:10.1109/83.661192. PMID 18276262.. ... "Advances in Neural Information Processing Systems 14 (NIPS). NIPS. MIT Press. pp. 221-228. Black:ANIPS:2002.. ...
"Vibrotactile sensitivity threshold: nonlinear stochastic mechanotransduction model of the Pacinian Corpuscle". IEEE ... Processes and concepts. Sensation. *Stimulus. *Sensory receptor. *Transduction (physiology). *Sensory processing. *Active ...
... has applications in statistical inference. For example, one might use it to fit an isotonic curve to the means of some set of experimental results when an increase in those means according to some particular ordering is expected. A benefit of isotonic regression is that it is not constrained by any functional form, such as the linearity imposed by linear regression, as long as the function is monotonic increasing. Another application is nonmetric multidimensional scaling,[1] where a low-dimensional embedding for data points is sought such that order of distances between points in the embedding matches order of dissimilarity between points. Isotonic regression is used iteratively to fit ideal distances to preserve relative dissimilarity order. Software for computing isotone (monotonic) regression has been developed for the R statistical package [2], the Stata statistical package and the Python programming language [3]. ...
連續時間(英語:Continuous-time stochastic process). *Bessel process(英語:Bessel process) ... Point process(英語:Point process) *Cox(英語:Point process#Cox point process) ... Galton-Watson process(英語:Galton-Watson process). *Independent and identically distributed random variables(英語:Independent and ... 離散時間(英語:Discrete-time stochastic process). *伯努
For the process of speaking to a group of people, see Public speaking. For other uses, see Speech (disambiguation). ... Diseases and disorders of the brain, including alogia, aphasias, dysarthria, dystonia and speech processing disorders, where ... In addition to dysphasia, anomia and auditory processing disorder can impede the quality of auditory perception, and therefore ... Speech perception refers to the processes by which humans can interpret and understand the sounds used in language. The study ...
He is best known for his work on stochastic processes.[216][217] ... and Markov processes. The Dynkin diagram, the Dynkin system, ... the Oppenheimer-Phillips process in nuclear fusion, and the first prediction of quantum tunneling. With his students he made ...
More complex experiments, such as those involving stochastic processes defined in continuous time, may demand the use of more ... Related to events in a Poisson process (events that occur independently with a given rate)[edit]. *Poisson distribution, for ... The following is a list of some of the most common probability distributions, grouped by the type of process that they are ... The cache language models and other statistical language models used in natural language processing to assign probabilities to ...
The process of securitization is complex and depends greatly on the jurisdiction within which the process is conducted. Among ... These are also required in most models that specify the credit risk as a stochastic function with an IR correlation. ... system wherein underlying mortgages were assigned and reassigned outside of the traditional county-level recording process. The ...
Specific epigenetic processes include paramutation, bookmarking, imprinting, gene silencing, X chromosome inactivation, ... A simplified stochastic model for this type of epigenetics is found here.[59][60] ... Mattick JS, Amaral PP, Dinger ME, Mercer TR, Mehler MF (January 2009). "RNA regulation of epigenetic processes". BioEssays. 31 ... One example of an epigenetic change in eukaryotic biology is the process of cellular differentiation. During morphogenesis, ...
is the restoring force which is opposed to the stochastic force f. (. t. ). {\displaystyle f(t)}. due to the Brownian motion. ... Other applications are the rheology of soft matter, and studies of force-regulated processes in living cells. Forces are ... The power spectral density of the stochastic force F. (. ω. ). {\displaystyle F(\omega )}. can be derived by using the ... Due to anisotropies in the stochastic distribution of the nanoparticles within the microbead the magnetic moment is not ...
Random sampling is a related, but distinct process.[1] Random sampling is recruiting participants in a way that they represent ...
... recruiting the best graduates from Oxford and Cambridge to apply biochemistry and statistics to Guinness's industrial processes ...
Stochastic processes[edit]. The reproducibility requirement cannot be applied to individual samples of phenomena which have a ... Interdisciplinary research : process and theory. Szostak, Rick, 1959- (Third edition ed.). Los Angeles. ISBN 9781506330488. ... Stanley Pons and Martin Fleischmann reported the production of excess heat that could only be explained by a nuclear process (" ...
This process determines which activities are "critical" (i.e., on the longest path) and which have "total float" (i.e., can be ... In addition, the method can easily incorporate the concepts of stochastic predictions, using the program evaluation and review ... Since then, it has been expanded to allow for the inclusion of resources related to each activity, through processes called ... Critical path drag analysis has also been used to optimize schedules in processes outside of strict project-oriented contexts, ...
There are two components to this process: a) stochastic over- and under-amplification of random regions; and b) systematic bias ... The stochastic component may be addressed by pooling single-cell MDA reactions from the same cell type, by employing ... allowing the entire process of development to be mapped on a cell-by-cell basis. Science recognized these advances as the 2018 ... Data obtained from microorganisms might establish processes for culturing in the future.[10] Some of the genome assembly tools ...
In mathematics, the law of a stochastic process is the measure that the process induces on the collection of functions from the ... Let X : T × Ω → S be a stochastic process (so the map X t : Ω → S : ω ↦ X ( t , ω ) {\displaystyle X_{t}:\Omega \to S:\omega \ ... The law encodes a lot of information about the process; in the case of a random walk, for example, the law is the probability ... Indeed, many authors define Brownian motion to be a sample continuous process starting at the origin whose law is Wiener ...
Stochastic Processes and Filtering Theory. New York: Academic Press. ISBN 0-12-381550-9. Øksendal, Bernt K. (2003). Stochastic ... In the theory of stochastic processes, the filtering problem is a mathematical model for a number of state estimation problems ... Maybeck, Peter S., Stochastic models, estimation, and control, Volume 141, Series Mathematics in Science and Engineering, 1979 ... Filtering (disambiguation) Not to be confused with Filter (signal processing) Kalman filter most famous filtering algorithm in ...
... stochastic process (en); عملية تصادفية (ar); فرایند تصادفی (fa); Proces stochastic (ro) concepto matemático (es); description ... Media in category "Stochastic processes". The following 159 files are in this category, out of 159 total. ... stochastic process mathematical object usually defined as a collection of random variables ... Pages in category "Stochastic processes". This category contains only the following page. ...
Stochastic processes are defined and grouped into different classes, their basic properties are listed and compared. The ... Schuster P. (2016) Stochastic Processes. In: Stochasticity in Processes. Springer Series in Synergetics. Springer, Cham. https ... Moyal, J.E.: Stochastic processes and statistical physics. J. R. Stat. Soc. B 11, 150-210 (1949)MathSciNetzbMATHGoogle Scholar ... Karlin, S., Taylor, H.M.: A First Course in Stochastic Processes, 2nd edn. Academic Press, New York (1975)zbMATHGoogle Scholar ...
Probability theory and stochastic processes. Probability theory and stochastic processes. .addthis_counter.addthis_bubble_style ... Receive email alerts on new books, offers and news in Probability theory and stochastic processes. ... Quantum Fields and Processes A Combinatorial Approach. Gough, John Kupsch, Joachim Published: April 2018Published: March 2018 ... Stochastic Dynamics, Filtering and Optimization Roy, Debasish Rao, G. Visweswara Published: May 2017Published: January 2018 ...
This book presents applied probability and stochastic processes in an elementary but mathematically precise manner, with ... Carlo Simulation Poisson Processes Poisson process Queueing Networks Queueing Processes Replacement Theory stochastic process ... Inventory Theory Markov Chains Markov Decicision Processes Markov Processes Markov chain Markov decision process Markov process ... This book presents applied probability and stochastic processes in an elementary but mathematically precise manner, with ...
1 Markov Processes and Transition Functions, 156 2 Markov Jump Processes and Feller Processes, 162 3 The Martingale Problem: ... Galton-Watson Processes, 386 Two-Type Markov Branching Processes, 392 Branching Processes in Random Environments, 396 Branching ... Stochastic Processes, 49 Martingales, 55 Local Martingales, 64 The Projection Theorem, 71 The Doob-Meyer Decomposition, 74 ... Markov Processes in Zd, 329 Diffusion Processes, 328 Problems, 332 Notes, 335 337 ...
Stochastic Processes in Physics and Engineering. [Sergio Albeverio; Philip Blanchard; Michiel Hazewinkel; Ludwig Streit] -- ... Stochastic Processes in Physics and Engineering. Author:. Sergio Albeverio; Philip Blanchard; Michiel Hazewinkel; Ludwig Streit ... Stochastic Processes in Physics and Engineering/Sergio Albeverio; Philip Blanchard; Michiel Hazewinkel; Ludwig Streit; ... Add tags for "Stochastic Processes in Physics and Engineering". Be the first. ...
Two masking processes were found: an early process affected by physical factors such as adapting luminance and a later process ... Stochastic modeling of elementary psychological processes. Cambridge: (1983) by J T Townsend, F G Ashby ... The present data are compatible with a parallel but limited-resource process, though not with a strictly serial process ofthe ... Outliers are response times generated by processes that are not the ones being studied. The processes that generate out-liers ...
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... this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations ... An introduction to sparse stochastic processes. [Michael A Unser; Pouya Tafti] -- Providing a novel approach to sparsity, ... to the modern sparsity-based view on signal processing, as well as stochastic processes. Some of the early motivations given by ... to the modern sparsity-based view on signal processing, as well as stochastic processes. Some of the early motivations given by ...
... In this seminar, we will discuss some of the main themes ... Disciplines with similar materials as 7.342 Systems Biology: Stochastic Processes and Biological Robustness. ... that have arisen in the field of systems biology, including the concepts of robustness, stochastic cell-to-cell variability, ...
161-171 Local linear estimation for stochastic processes driven by $$\alpha $$ α -stable L $$\acute{\mathbf{e}}$$ e ´ vy motion ... 323-357 Estimation of the lead-lag parameter between two stochastic processes driven by fractional Brownian motions. by Kohei ... 347-367 The asymptotics of misspecified MLEs for some stochastic processes: a survey. by Yury A. Kutoyants * 369-386 ... 637-663 The robust focused information criterion for strong mixing stochastic processes with $$\mathscr {L}^{2}$$ L 2 - ...
The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov ... Featuring a logical combination of traditional and complex theories as well as practices, Probability and Stochastic Processes ... A comprehensive and accessible presentation of probability and stochastic processes with emphasis on key theoretical concepts ... Probability and Stochastic Processes is also an ideal reference for researchers and practitioners in the fields of mathematics ...
... present a survey of the many ways in which the statistical package GLIM may be used to model and analyze stochastic processes. ... The Analysis of Stochastic Processes using GLIM. Authors. * James K. Lindsey Series Title. Lecture Notes in Statistics. Series ... present a survey of the many ways in which the statistical package GLIM may be used to model and analyze stochastic processes. ...
Theory of Stochastic Objects Probability, Stochastic Processes and Inference By Athanasios Christou Micheas. ... through probability in Chapter 4 and stochastic notions of convergence in Chapter 5, to stochastic processes in discrete ( ... This volume provides an approachable introduction to the theory of probability and stochastic processes; together with its ... as well as stochastic processes - in addition to at least one text on statistics- to capture the detail and depth of material ...
Title: Download Discretization of Processes (Stochastic Modelling and Applied Probability) Ebook, Author: jbnote18108, Name: ... Download Discretization of Processes (Stochastic Modelling and Applied Probability) Ebook, Length: 1 pages, Page: 1, Published ... Download Discretization of Processes (Stochastic Modelling and Applied Probability) Ebook. Discretization of Processes ( ... Stochastic Modelling and Applied Probability) Ebook, {Read,Download} Full PDF Download Discretization of Processes (Stochastic ...
A Graphical Representation for Biological Processes in the Stochastic Pi-calculus November 1, 2006 ... The graphical representation can also be used as a front end to a simulator for the stochastic pi-calculus. This complements ... This paper presents a graphical representation for the stochastic pi-calculus, which builds on previous formal and informal ...
In todays world, where computers are part of any scientific ... - Selection from Probability and Stochastic Processes [Book] ... Probability and Stochastic Processes by Ionut Florescu. Stay ahead with the worlds most comprehensive technology and business ...
... driven by a positive Levy process without Gaussian component. They also consider superpositions of such processes and we extend ... The algorithm is based on Markov chain Monte Carlo methods and we use a series representation of Levy processes. Inference for ... especially if a superposition of processes is used. After introducing some extra flexibility, the model can even be used to ... proposed a class of models where the volatility behaves according to an Ornstein-Uhlenbeck process, ...
Stochastic realizations are therefore defined purely in terms of successive event-time pairs, and such systems are easy to ... The vast majority of random processes in the real world have no memory - the next step in their development depends purely on ... Stochastic Population Processes: Analysis, Approximations, Simulations. Eric Renshaw. Abstract. The vast majority of random ... Keywords: random processes, stochastic realizations, even-time pairs, probability equations, approximation Bibliographic ...
Stochastic nature of gene transcription is widely known and its contribution to heterogeneity of cellular responses to various ... Therefore, many authors claim that mathematical models of this process should be stochastic and deterministic modeling is not ... Stochastic nature of gene transcription is widely known and its contribution to heterogeneity of cellular responses to various ... comparing the expected value of transcription rate and the resulting amount of mRNA obtained from a stochastic model with ...
60G: Stochastic processes *60H: Stochastic analysis, see also 58G32 *60J: Markov processes *60K: Special processes This is one ... Applications to repeated transitions or transitions over time lead to Markov processes and stochastic processes. Probability ... "What is a stochastic process?", Amer. Math. Monthly 49 (1942) 648--653. MR4,103b Resnick, Sidney: "Adventures in stochastic ... Pointers regarding stochastic differential equations. *Pointers for information on branching processes. *The Secretary Problem ...
Introduction to Markov and Gaussian processes, stationary processes, spectral representation, ergodicity, renewal processes, ... Probability spaces, random variables, expectation, conditional expectation, stochastic convergence, characteristic functions, ... stochastic convergence, characteristic functions, and limit theorems. ...
... builds directly upon early-twentieth-century explanations of the "peculiar ... An Introduction to Stochastic Processes in Physics] presents fundamental ideas with admirable clarity and concision. The author ... "Professor Lemonss book has reclaimed the field of stochastic processes for physics. For too long it has been taught as a ... "Self-contained and provides adequate insight into stochastic processes in physics. It is quite readable and will be useful to ...
Probability and Stochastic Processes by Ionut Florescu. Stay ahead with the worlds most comprehensive technology and business ... Conditional expectation is a fundamental concept in the theory of stochastic processes. The simple idea is the following: ... Selection from Probability and Stochastic Processes [Book] ...
Stochastic Art: 10.4018/978-1-4666-8142-2.ch006: The objective of this chapter is to help solve a classic stochastic problem ... A random process, also called a stochastic process, is a collection of random variables defined on an underlying probability ... "Random Processes and Visual Perception: Stochastic Art." In Handbook of Research on Maximizing Cognitive Learning through ... "Random Processes and Visual Perception: Stochastic Art." Handbook of Research on Maximizing Cognitive Learning through ...
Elementary Probability Theory: With Stochastic Processes and an Introduction .... K. L. Chung,Farid AitSahlia. No preview ... Elementary Probability Theory: With Stochastic Processes and an Introduction .... Kai Lai Chung,Farid AitSahlia. Limited ... Elementary Probability Theory: With Stochastic Processes and an Introduction to Mathematical Finance. Undergraduate Texts in ... Elementary Probability Theory: With Stochastic Processes and an Introduction to Mathematical Finance. ...
Two types of processes-deterministic and stochastic-influence the assembly of species into communities. Deterministic processes ... 2011) Stochastic and deterministic processes interact in the assembly of desert microbial communities on a global scale. ISME J ... 2012) Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J 6(9):1653-1664. ... 11). As opposed to a dichotomous debate, in which one attempts to reject stochastic processes in favor of deterministic ones ( ...
Quantum Independent Increment Processes: Structure and Applications to Physics. This school was held at the Alfried-Krupp- ... Quantum Independent Increment Processes I. Book Subtitle. From Classical Probability to Quantum Stochastic Calculus. Authors. * ... Quantum Independent Increment Processes I. From Classical Probability to Quantum Stochastic Calculus. Authors: Applebaum, D., ... He now works on classical Lévy processes and stochastic flows on manifolds driven by Lévy type noise. Earlier this year he ...
  • This book presents applied probability and stochastic processes in an elementary but mathematically precise manner, with numerous examples and exercises to illustrate the range of engineering and science applications of the concepts. (springer.com)
  • The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov chains, semi-Markov processes, martingales, and Brownian motion. (recordedbooks.com)
  • This site lists free online lecture notes and books on stochastic processes and applied probability, stochastic calculus, measure theoretic probability, probability distributions, Brownian motion, financial mathematics, Markov Chain Monte Carlo, martingales. (uwindsor.ca)
  • Starting with the construction of stochastic processes, the book introduces Brownian motion and martingales. (oup.com)
  • Chapter 3 covers discrete stochastic processes and Martingales. (freecomputerbooks.com)
  • Examples of such stochastic processes include the Wiener process or Brownian motion process, used by Louis Bachelier to study price changes on the Paris Bourse, and the Poisson process, used by A. K. Erlang to study the number of phone calls occurring in a certain period of time. (wikipedia.org)
  • Many later mathematical stochastic processes models have been developed in the context of studying Brownian motion. (igi-global.com)
  • I am working on some research but have to justify the following argument: Assume $S_t$ is a continuous stochastic process, don't want to make an assumption about distribution, think about something like a smooth function of Brownian motion. (mathoverflow.net)
  • Chapter 4 covers continuous stochastic processes like Brownian motion up to stochstic differential equations. (freecomputerbooks.com)
  • The two types of stochastic processes are respectively referred to as discrete-time and continuous-time stochastic processes. (wikipedia.org)
  • I am trying to understand various types of stochastic processes. (stackexchange.com)
  • I am trying to understand various types of stochastic processes through some analogical examples using simple experiments like a coin toss or die roll. (stackexchange.com)
  • Kindly, give me some analogies for these types of stochastic processes. (stackexchange.com)
  • The theory of stochastic processes is considered to be an important contribution to mathematics and it continues to be an active topic of research for both theoretical reasons and applications. (wikipedia.org)
  • Stochastic Portfolio Theory is a framework in which the normative assumptions from classical financial mathematics are not made, but in which one takes a descriptive approach to studying properties of markets that follow from empirical observations. (e-booksdirectory.com)
  • More interestingly still, the Poisson process can be viewed very similarly and since the Poisson mathematics are derived from the Binomial process where n is made large and p is small, there is a strong relationship between the corresponding Poisson and Binomial distributions. (vosesoftware.com)
  • The lecture will be given annually and will be featured at meetings (co)-sponsored by the IMS or the Bernoulli Society with a strong attendance by researchers in probability and stochastic processes. (imstat.org)
  • The wide selection of topics makes the book accessible to advanced graduate students and researchers in probability and stochastic processes. (bokkilden.no)
  • The pendant to FP equations on the discontinuous side are master equations which deal only with jump processes and represent the appropriate tool for modeling processes described by discrete variables. (springer.com)
  • Particular emphasis is laid on modeling conventional and anomalous diffusion processes. (springer.com)
  • Citation Query Stochastic modeling of elementary psychological processes. (psu.edu)
  • Stochastic modeling of elementary psychological processes. (psu.edu)
  • Therefore, many authors claim that mathematical models of this process should be stochastic and deterministic modeling is not acceptable. (actapress.com)
  • There is a node in the GAMS software tree for Simulation, stochastic modeling . (math-atlas.org)
  • In particular, we focus on statistical modeling via two flexible random processes: Markov processes and Gaussian processes. (ucsc.edu)
  • Next, we consider an alternative stochastic process that is widely applied in temporal data modeling, the Gaussian process. (ucsc.edu)
  • Much has changed in the field of stochastic modeling since the highly successful Second Edition of this popular text. (abebooks.com)
  • Finally, we discuss numerical implementation of pricing formulas and apply the considered processes for modeling the DAX options volatility surface. (ssrn.com)
  • This project is aiming at taking a step further in the process of systematic statistical modeling that is occurring in the field of comparative ecology. (archives-ouvertes.fr)
  • Due to environmental changes, some ecological niches can shift in time, inducing a shift in the parameters values of the stochastic process modeling trait evolution. (archives-ouvertes.fr)
  • This talk will present the recent work on the stochastic modeling of reaction-diffusion processes in glucose metabolism. (upenn.edu)
  • Next, attention is focused on stochastic differential equations (SDEs) which arise in modeling physical phenomena, perturbed by random forces. (oup.com)
  • We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. (pubmedcentralcanada.ca)
  • Then located as the Stochastic for the Modeling of knees. (dbehringer.de)
  • We have developed discrete-state stochastic and continuum mean field approaches to investigate the corresponding reaction-diffusion models. (rice.edu)
  • Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. (worldcat.org)
  • Recently, Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein-Uhlenbeck process, driven by a positive Levy process without Gaussian component. (repec.org)
  • Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility ," Journal of Econometrics , Elsevier, vol. 134(2), pages 605-644, October. (repec.org)
  • Motivated by the kernel perspective of inducing-point based sparse Gaussian processes, we propose a general regularization framework of sparse Gaussian processes and extend it into latent variable models. (ucsc.edu)
  • Building on our proposed regularization framework, we develop a hierarchical sparse latent Gaussian process model specifically for categorical data and then we extend our model to temporal data via dynamical priors. (ucsc.edu)
  • We propose a novel nonstationary multivariate Gaussian process model that allows it to model time dependent smoothness, scale and correlation across different dimensions. (ucsc.edu)
  • The Stroock-Varadhan martingale problem, the connection between diffusion processes and partial differential equations, Gaussian solutions of SDEs, and Markov processes with jumps are presented in successive chapters. (oup.com)
  • The variational framework for learning inducing variables (Titsias, 2009a) has had a large impact on the Gaussian process literature. (lancs.ac.uk)
  • Stochastic processes include probabilistic dispersal and random changes in species relative abundances (ecological drift) that are not the consequence of environmentally determined fitness ( 5 , 6 ). (pnas.org)
  • Stochastic processes are probabilistic models for random quantities evolving in time or space. (jhu.edu)
  • The breadth and power of Stochastic Analysis, and probabilistic behavior of diffusion processes are told without compromising on the mathematical details. (oup.com)
  • Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. (worldcat.org)
  • Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. (cam.ac.uk)
  • In this talk, I will show how the Poisson representation of the Chemical Master Equation can be used to derive a novel connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes. (newton.ac.uk)
  • A practical introduction to stochastic modelling of reaction-diffusion processes is presented. (arxiv.org)
  • These two stochastic processes are considered the most important and central in the theory of stochastic processes, and were discovered repeatedly and independently, both before and after Bachelier and Erlang, in different settings and countries. (wikipedia.org)
  • Conditional expectation is a fundamental concept in the theory of stochastic processes. (oreilly.com)
  • This Third Edition of Elements of Applied Stochastic Processes provides a basic understanding of the fundamental theory of stochastic processes. (abebooks.com)
  • The initial chapters present a summary of probability and statistics and then Poisson processes, Markov chains, Markov processes and queuing processes are introduced. (springer.com)
  • Applications to repeated transitions or transitions over time lead to Markov processes and stochastic processes. (math-atlas.org)
  • Markov processes and their transition-probability semi-groups. (cmu.edu)
  • Math 632 gives an introduction to Markov chains and Markov processes with discrete state spaces and their applications. (wisc.edu)
  • It serves as a reference for researchers who use probability models based on Markov processes, renewal processes and time series, and aids consultants who solve problems involving probability models in the societal, industrial, business, and government sectors. (abebooks.com)
  • The mograph is devoted mainly to the analytical study of the differential, pseudo-differential and stochastic evolution equations describing the transition probabilities of various Markov processes. (ebay.com.au)
  • This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. (pandora.com.tr)
  • Presents and illustrates 'random objects' in different contexts, under a unified framework, starting with rudimentary results on random variables and random sequences, all the way up to stochastic partial differential equations. (routledge.com)
  • The book features articles drawn from different research areas in probability and stochastic processes, such as: risk theory, limit theorems, stochastic partial differential equations, random trees, stochastic differential games, stochastic control, and coalescence. (bokkilden.no)
  • Discretization of Processes (Stochastic Modelling and Applied Probability) Download Click This Link http://news.edubooks.site/?book=364224. (issuu.com)
  • The study of stochastic processes uses mathematical knowledge and techniques from probability, calculus, linear algebra, set theory, and topology as well as branches of mathematical analysis such as real analysis, measure theory, Fourier analysis, and functional analysis. (wikipedia.org)
  • This paper presents a graphical representation for the stochastic pi-calculus, which builds on previous formal and informal notations. (microsoft.com)
  • The graphical representation can also be used as a front end to a simulator for the stochastic pi-calculus. (microsoft.com)
  • Browse other questions tagged stochastic-processes stochastic-calculus stochastic-filtering or ask your own question . (mathoverflow.net)
  • Ocone, Daniel L.: "A guide to the stochastic calculus of variations", Stochastic analysis and related topics (Silivri, 1986), 1--79, Lecture Notes in Math. (math-atlas.org)
  • the Wiener process, the functional central limit theorem, and the elements of stochastic calculus. (cmu.edu)
  • Stochastic Analysis and Diffusion Processes presents a simple, mathematical introduction to Stochastic Calculus and its applications. (oup.com)
  • Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. (wikipedia.org)
  • In special cases, the model becomes equivalent to a classical diffusion process, but leakage and mutual inhibition work together to address several challenges to existing diffusion, random-walk, and accumulator models. (psu.edu)
  • The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. (worldcat.org)
  • The ambition is to integrate, from the very beginning, probability with statistical ideas, frequentists and Bayesian, to build an understanding of why, and how to construct stochastic models. (routledge.com)
  • Continuous-time stochastic volatility models are becoming a more and more popular way to describe moderate and high-frequency financial data. (repec.org)
  • Inference for such models is complicated by the fact that parameter changes will often induce a change of dimension in the representation of the process and the associated problem of overconditioning. (repec.org)
  • An application to stock price data shows the models perform very well, even in the face of data with rapid changes, especially if a superposition of processes is used. (repec.org)
  • Bayesian Analysis of Stochastic Volatility Models ," Journal of Business & Economic Statistics , American Statistical Association, vol. 20(1), pages 69-87, January. (repec.org)
  • Bayesian Analysis of Stochastic Volatility Models ," Journal of Business & Economic Statistics , American Statistical Association, vol. 12(4), pages 371-389, October. (repec.org)
  • The results indicate that deterministic models can be used only if transcription factors that initiate the transcription process weakly bind to their respective promoter regions in DNA. (actapress.com)
  • This work mainly emphasizes efficient statistical models on temporal data via stochastic processes. (ucsc.edu)
  • Stochastic Models course. (uwindsor.ca)
  • Particular models studied include birth-death chains, queuing models, random walks and branching processes. (wisc.edu)
  • Completely new chapters in the second half of the book include: queueing networks, communication and information systems, inventory and storage processes, combat models, Markov models in biological sciences, and stochastic models in traffic flow theory and geological sciences. (abebooks.com)
  • Stochastic process models are used extensively in operations research applications. (jhu.edu)
  • Shifted stochastic processes evolving on trees : application to models of adaptive evolution on phylogenies. (archives-ouvertes.fr)
  • These include (i) diffusions (in particular,degenerate diffusions), (ii) more general jump-diffusions, especially stable jump-diffusions driven by stable Levy processes, (iii) complex stochastic Schrodinger equations which correspond to models of quantum open systems. (ebay.com.au)
  • Stochastic models are used to describe those processes that are not completely understood or are too complicated to be described deterministically ( 13 ). (asm.org)
  • In our case, the first division times for a sufficiently large number of cells could be measured, but the complexity and the lack of knowledge about the intracellular processes during the lag were the main reasons for turning to stochastic models. (asm.org)
  • To gain mechanistic insights, the investigators evaluated five mathematical models relating the shape of the correlation functions with the timing of the underlying molecular processes. (cancer.gov)
  • Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. (pubmedcentralcanada.ca)
  • The connections between stochastic simulations and deterministic models are explained and basic mathematical tools (e.g. chemical master equation) are presented. (arxiv.org)
  • Mathematical models of physical processes invariably include unknown parameters, which need to be estimated from real data. (warwick.ac.uk)
  • We are considered this Stochastic Processes and Models to Thank you little book in according with the ã of your failures), whether that lost not or a first Buddhist too. (dbehringer.de)
  • Stochastic Processes and Models does collaborating to just Get Rotating your review. (dbehringer.de)
  • The Stochastic Processes and Models will Notify some of the scale book in the Hebrew Bible, ritual on the self of the Woman Wisdom and the new Support, in challenge with two books of Job. (dbehringer.de)
  • We apply Milstein scheme for solving the stochastic models numerically. (ump.edu.my)
  • Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits. (ump.edu.my)
  • Also new in this edition are an introductory chapter on statistics and a chapter on Poisson processes that includes some techniques used in risk assessment. (springer.com)
  • This section describes four very fundamental stochastic processes: the Binomial , Poisson , Hypergeometric , and Central Limit Theorem (CLT). (vosesoftware.com)
  • This section also discusses a generalization of the Poisson process where the times between events are independent and identically distributed with an arbitrary distribution, a type of randomness known as a renewal process which is often used in modelling equipment reliability, for example. (vosesoftware.com)
  • The chapter on Poisson processes has moved up From third to second, and is now Followed by a treatment oF the closely related topic oF renewal theory. (coursehero.com)
  • In this second part, we analyze the dissipation properties of Generalized Poisson-Kac (GPK) processes, considering the decay of suitable $L^2$-norms and the definition of entropy functions. (arxiv.org)
  • By determining the first jump moment of this process we abstract the dynamic of the mean evolutionary path. (iiasa.ac.at)
  • Professor Lemons's book has reclaimed the field of stochastic processes for physics. (jhu.edu)
  • statistical inference, measure-theoretic probability theory and stochastic processes. (routledge.com)
  • I will also presume some familiarity with basic stochastic processes, at the level of 36-703 ("Intermediate Probability"), though I will not assume those memories are very fresh. (cmu.edu)
  • Finally, basic stochastic reaction-diffusion methods are presented. (arxiv.org)
  • 10 Renewal Process. (uwindsor.ca)
  • Chapters 1 through 9 treat the theory of Markov, renewal, and stationary processes. (abebooks.com)
  • Topics include Markov chains, and Markov, branching, renewal, and stationary processes, all of which are illustrated with the rich diversity of actual applications. (abebooks.com)
  • This book provides an accessible introduction to stochastic processes in physics and describes the basic mathematical tools of the trade: probability, random walks, and Wiener and Ornstein-Uhlenbeck processes. (jhu.edu)
  • In this PhD thesis, we first study the (planar) complex valued Ornstein-Uhlenbeck processes (Zt = Xt + iYt, t ≥ 0), where (Xt, t ≥ 0) and (Yt, t ≥ 0) denote its cartesian coordinates. (archives-ouvertes.fr)
  • We develop some identities in law in terms of (planar) complex valued Ornstein-Uhlenbeck processes, which are equivalent to Bougerol's identity. (archives-ouvertes.fr)
  • 0) of the continuous winding processes θt, t ≥ 0 associated with our complex Ornstein-Uhlenbeck process. (archives-ouvertes.fr)
  • When interpreted as time, if the index set of a stochastic process has a finite or countable number of elements, such as a finite set of numbers, the set of integers, or the natural numbers, then the stochastic process is said to be in discrete time. (wikipedia.org)
  • Discrete-time stochastic processes are considered easier to study because continuous-time processes require more advanced mathematical techniques and knowledge, particularly due to the index set being uncountable. (wikipedia.org)
  • Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes. (ellibs.com)
  • This book provides an introduction to probability theory and discrete and continuous stochastic processes and its applications. (freecomputerbooks.com)
  • The book proceeds to construct stochastic integrals, establish the Ito formula, and discuss its applications. (oup.com)
  • In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. (pubmedcentralcanada.ca)
  • Then stochastic algorithms for modelling molecular diffusion are given. (arxiv.org)
  • Browse other questions tagged probability-distributions stochastic-processes markov-chains expected-value gambling or ask your own question . (stackexchange.com)
  • Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. (pubmedcentralcanada.ca)
  • Stochastic Differential Equations: An Introduction with Applications (Sixth ed. (wikipedia.org)
  • An Introduction to Stochastic Processes in Physics builds directly upon early-twentieth-century explanations of the "peculiar character in the motions of the particles of pollen in water" as described, in the early nineteenth century, by the biologist Robert Brown. (jhu.edu)
  • An Introduction to Stochastic Processes in Physics ] presents fundamental ideas with admirable clarity and concision. (jhu.edu)
  • 2007. Introduction to Stochastic Processes. (uwindsor.ca)
  • His work had a profound impact on probability-he transformed our understanding of planar processes from statistical physics through his introduction of the Schramm-Loewner evolution (SLE), tying probability theory to complex analysis in a completely novel way. (imstat.org)
  • He is the author of Stochastic Filtering Theory , and a co-author of White Noise Theory of Prediction, Filtering and Smoothing , Introduction to Option Pricing Theory , and Stochastic Differential Equations in Infinite Dimensions . (oup.com)
  • An Introduction to Stochastic Differential Equations. (helsinki.fi)
  • Some recent developments in stochastic volatility modelling ," Quantitative Finance , Taylor & Francis Journals, vol. 2(1), pages 11-23. (repec.org)
  • First, we consider a wide class of affine jump-diffusions proposed for the asset price dynamics: jump-diffusions, diffusions with stochastic volatility, jump-diffusions with stochastic volatility, and jump-diffusions with stochastic volatility and jump intensity. (ssrn.com)
  • Sepp, Artur, Pricing European-Style Options under Jump Diffusion Processes with Stochastic Volatility: Applications of Fourier Transform (September 7, 2003). (ssrn.com)
  • Across ecology, and particularly within microbial ecology, there is limited understanding of the mechanisms governing the relative influences of stochastic and deterministic processes. (pnas.org)
  • Ecological succession and the balance between stochastic and deterministic processes are two major themes within microbial ecology, but these conceptual domains have mostly developed independent of each other. (pnas.org)
  • As applications we use small Ising model simulations and a larger medical image processing algorithm. (spie.org)
  • No prior knowledge of stochastic simulations is assumed. (arxiv.org)
  • Numerical solution of stochastic differential equations. (wikipedia.org)
  • On the other hand, spatio-temporal point processes offer several computational advantages from the statistical perspective, and can be coupled to dynamics via an evolving intensity field. (cam.ac.uk)
  • The evolutionary dynamics of influenza virus ultimately derive from processes that take place within and between infected individuals. (elifesciences.org)
  • Browse other questions tagged probability random-variable stochastic-processes or ask your own question . (stackexchange.com)
  • Sample continuity is the appropriate notion of continuity for processes such as Itō diffusions. (wikipedia.org)
  • Stochastic differential equations (SDEs) model processes at the level of random variables by solving ordinary differential equations upon which a diffusion process, called a Wiener process, is superimposed. (springer.com)
  • In probability theory and related fields, a stochastic (/stoʊˈkæstɪk/) or random process is a mathematical object usually defined as a family of random variables. (wikipedia.org)
  • A stochastic process may involve several related random variables. (wikipedia.org)
  • Furthermore, seemingly random changes in financial markets have motivated the extensive use of stochastic processes in finance. (wikipedia.org)
  • The term random function is also used to refer to a stochastic or random process, because a stochastic process can also be interpreted as a random element in a function space. (wikipedia.org)
  • The terms stochastic process and random process are used interchangeably, often with no specific mathematical space for the set that indexes the random variables. (wikipedia.org)
  • A stochastic or random process can be defined as a collection of random variables that is indexed by some mathematical set, meaning that each random variable of the stochastic process is uniquely associated with an element in the set. (wikipedia.org)
  • A stochastic process can be classified in different ways, for example, by its state space, its index set, or the dependence among the random variables. (wikipedia.org)
  • The relationships between the various types of continuity of stochastic processes are akin to the relationships between the various types of convergence of random variables. (wikipedia.org)
  • Random Processes and Visual Perception: Stochastic Art. (igi-global.com)
  • A random process, also called a stochastic process, is a collection of random variables defined on an underlying probability space. (igi-global.com)
  • Stochastic processes are collections of interdependent random variables. (cmu.edu)
  • Definition of stochastic processes, examples, random functions. (cmu.edu)
  • 2006. Random Processes. (uwindsor.ca)
  • The main results of the book concern the existence, two-sided estimates, path integral representation, and small time and semiclassical asymptotics for the Green functions (or fundamental solutions) of these equations, which represent the transition probability densities of the corresponding random process. (ebay.com.au)
  • Here, we will model the multiplication of cells by a stochastic birth process based on a set of random variables whose realizations are time dependent. (asm.org)
  • A stochastic process is a system of countable events, where the events occur according to some well-defined random process. (vosesoftware.com)
  • These are random processes where one or more of the defining parameters (like a binomial probability, for example) may itself be a random variable. (vosesoftware.com)
  • Also, the previous question (which also asked for examples of all eight kinds of process listed here before it was edited) had details of what the OP understood about random processes, and these details revealed that the OP had some misconceptions. (stackexchange.com)
  • Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables. (wakehealth.edu)
  • I define another process $$Y_t=\frac{1}{t} \int_0^t S_u du$$ Now I am interested in the limit of $Y_t$ as $t$ approaches zero. (mathoverflow.net)
  • Applications and the study of phenomena have in turn inspired the proposal of new stochastic processes. (wikipedia.org)
  • A comprehensive and accessible presentation of probability and stochastic processes with emphasis on key theoretical concepts and real-world applications With a sophisticated approach, Probability and Stochastic Processes successfully balances theory and applications in a pedagogical and accessible format. (recordedbooks.com)
  • The 29th Conference on Stochastic Processes and their Applications is organized under the auspices of the Bernoulli Society for Mathematical Statistics and Probability It will be held from 3 to 9 August, 2003 at the Hotel do Frade, Angra dos Reis, Rio de Janeiro, Brazil. (impa.br)
  • This revised and expanded edition of Elements of Applied Stochastic Processes offers wider coverage of the applications of stochastic processes in various fields. (abebooks.com)
  • Restructured to enhance the book's usefulness for practicing professionals, students, and instructors, this edition features two chapters dedicated entirely to applications from journal articles and new material on statistical inference for stochastic processes, with inference on queues as an area of application. (abebooks.com)
  • It is anticipated that the first Schramm lecture will be delivered at the Stochastic Processes and their Applications meeting to be held in Boulder, Colorado in 2013 (see http://math.colorado.edu/spa2013/ ). (imstat.org)
  • The book is written for graduate students, young researchers and applied scientists who are interested in stochastic processes and their applications. (oup.com)
  • volume 3 advances and applications the stochastic case goes more illocutionary than road, and each unrest is a deployed non-Nazis. (rjackbalthazar.com)
  • volume 3 advances and applications the stochastic, overall from its design. (rjackbalthazar.com)
  • The benefit of the approach is that belief values associated with segmentation hypotheses can be used to guide the recognition process, and the recognition process can, in turn, exert influence on the belief values associated with segmentation hypotheses in the network. (spie.org)
  • To begin to investigate the kinetic relationship between transcription and splicing, Daniel Larson, Ph.D., of CCR's Laboratory of Receptor Biology and Gene Expression, and his colleagues employed a single-molecule RNA imaging approach to monitor production and processing of a human β-globin reporter gene in living cells. (cancer.gov)
  • This approach provides us with an excellent opportunity to become very familiar with a number of important distributions, and to see the relationships between them, even between the distributions of the different stochastic processes. (vosesoftware.com)
  • The time course of perceptual choice is discussed in a model based on gradual and stochastic accumulation of information in non-linear decision units with leakage (or decay of activation) and competition through lateral inhibition. (psu.edu)
  • The aim of this book is to present a survey of the many ways in which the statistical package GLIM may be used to model and analyze stochastic processes. (springer.com)
  • In this paper several biologically viable cases of transcription are investigated, comparing the expected value of transcription rate and the resulting amount of mRNA obtained from a stochastic model with corresponding outputs of its deterministic counterpart. (actapress.com)
  • To explore the commonality of concern between Science and Art and better understand stochastic processes, the authors use a graph theory reference model called the "shortest route problem" and add additional elements specific to the art-making process to highlight the relevance of interdisciplinary studies in the field of randomness and visual perception. (igi-global.com)
  • Synthesizing previous work, we devised a conceptual model that links ecosystem development to alternative hypotheses related to shifts in ecological assembly processes. (pnas.org)
  • To better understand mechanisms governing these patterns, we developed an ecological simulation model that revealed how changes in selective environments cause shifts in the stochastic/deterministic balance. (pnas.org)
  • Finally, we propose an extended-and experimentally testable-conceptual model integrating ecological assembly processes with primary and secondary succession. (pnas.org)
  • The first stage of the research involves a novel hidden Markov model based on Markov jump processes for cervical cancer screening test data. (ucsc.edu)
  • A strongly simplified lattice model of proteins has been found to be a powerful theoretical tool to simulate the dynamic process of the folding and unfolding transitions. (mexmat.ru)
  • The first part of the talk introduces a compartment-based model for a simple glycolytic pathway using a continuous-time Markov jump process, which describes system features at different scales of interest. (upenn.edu)
  • This way, a stochastic birth model in which the underlying distributions were measured experimentally was simulated. (asm.org)
  • The measured distributions for the successive division intervals of the single cells were used to model the growth of the population as a stochastic process. (asm.org)
  • Kinetic competition between these various processes has been proposed to regulate mRNA maturation, but this model could lead to multiple, randomly determined, or stochastic, pathways or outcomes. (cancer.gov)
  • In this model splicing occurs a specific amount of time after the 3' splice site is transcribed, while transcript release takes place after reaching the poly(A) site and a stochastic delay. (cancer.gov)
  • URDME uses U nstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the R eaction- D iffusion M aster E quation formalism to model the processes under study. (pubmedcentralcanada.ca)
  • A model of stochastic storage for dams is presented. (iiasa.ac.at)
  • Here we describe how a relatively sophisticated stochastic model for microtubule dynamic instability in the mitotic spindle can be developed starting with straightforward rules and simple programming code. (umn.edu)
  • In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. (ump.edu.my)
  • It provides more in-depth coverage of Markov chains and simple Markov process and gives added emphasis to statistical inference in stochastic processes. (abebooks.com)
  • The main theme of the meeting was to illustrate the use of stochastic processes in the study of topological problems in quantum physics and statistical mechanics. (springer.com)
  • Econ 103 compositionality 4: process 88( favorite 490 in Economics Define GDP and read between a false charge and an Statistical science. (scheinerman.net)
  • Recent theoretical work shows that quantum theory can reduce this memory requirement beyond ultimate classical limits, as measured by a process' statistical complexity, C . We experimentally demonstrate this quantum advantage in simulating stochastic processes. (sciencemag.org)
  • On the other hand, spatio-temporal point processes offer several computational advantages from the statistical perspective. (newton.ac.uk)
  • This text brings statistical tools to engineers and scientists who design and develop new products, new manufacturing systems and processes and who improve existing systems. (pandora.com.tr)
  • The book's primary focus is on key theoretical notions in probability to provide a foundation for understanding concepts and examples related to stochastic processes. (recordedbooks.com)
  • Examples of one-parameter processes. (cmu.edu)
  • What are the examples for stochastic processes in Electrical Engineering and Computer Science? (stackexchange.com)
  • Finally, some examples are given of mixture processes . (vosesoftware.com)
  • Examples from chaotic advection (standard map coupled to stochastic GPK processes) illustrate this phenomenon. (arxiv.org)
  • The algorithm is based on Markov chain Monte Carlo methods and we use a series representation of Levy processes. (repec.org)
  • However, current methods have been unable to tease apart the contributions of these processes at a single gene or on a time scale that could provide mechanistic insight. (cancer.gov)
  • Gardner, MK & Odde, DJ 2010, ' Stochastic simulation and graphic visualization of mitotic processes ', Methods , vol. 51, no. 2, pp. 251-256. (umn.edu)
  • The useful weekends bestselling these knees even are a day-to-day Stochastic Processes and Qualitative Methods in Public Health: A chair for strong period and including book in earthquakes and practical shadows. (dbehringer.de)
  • The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. (worldcat.org)
  • Finally we show how our framework sheds light on interdomain sparse approximations and sparse approximations for Cox processes. (lancs.ac.uk)
  • Many stochastic processes can be represented by time series. (wikipedia.org)
  • However, a stochastic process is by nature continuous while a time series is a set of observations indexed by integers. (wikipedia.org)
  • An increment is the amount that a stochastic process changes between two index values, often interpreted as two points in time. (wikipedia.org)
  • In probability theory, a continuous stochastic process is a type of stochastic process that may be said to be "continuous" as a function of its "time" or index parameter. (wikipedia.org)
  • Let (Ω, Σ, P) be a probability space, let T be some interval of time, and let X : T × Ω → S be a stochastic process. (wikipedia.org)
  • Undergraduate students and those wishing to learn about stochastic processes for the first time would enjoy the clear pedagogic presentation. (jhu.edu)
  • A major goal in microbial community ecology is to understand the processes that underlie observed patterns in species abundances across space and time ( 1 ⇓ - 3 ). (pnas.org)
  • It develops basic concepts and techniques and brings together a sampling of their uses for solving problems arising in queueing, reliability, inventory and computer communications, social and behavioral processes, and business management and time series analysis. (abebooks.com)
  • These studies have established that this process cannot be fully characterized by the mean protein production rate, 8 - 12 since cells exhibit fluctuations (i.e. noise) over time and diversity in numbers across populations, 13 which, among other things, generates phenotypic diversity. (pubmedcentralcanada.ca)
  • We find that the free end of the polymer satisfies a novel stochastic equation with a nonlinear time function. (archives-ouvertes.fr)
  • Because we only measure the value of the process at a single time point, for extant species, some evolutionary scenarios cannot be reconstructed, or have some identifiability issues, that we carefully study. (archives-ouvertes.fr)
  • In this talk, I will discuss how joint time marginals of a stochastic reaction-diffusion process can be approximated in a mean-field sense by a spatio-temporal Cox process. (cam.ac.uk)
  • Strictly speaking, a stochastic process is also concerned with the sequence in which the events occur in time, but we shall take the more usual broader definition to include counting systems where the order is of no importance. (vosesoftware.com)
  • Open tales of late download Stochastic interiority are discussed and and music ERM at managing etc. beyond 2 approximately, authors took exploring such slides and time with the workshops( musical to the within ponds book) to be across the level images. (ideamill.com)
  • Here we provide a framework that integrates shifts in community assembly processes with microbial primary succession to better understand mechanisms governing the stochastic/deterministic balance. (pnas.org)
  • Gillespie, D. T. ' Exact Stochastic Simulation of Coupled Chemical Reactions . (mit.edu)
  • The article starts with the classical Gillespie algorithm for the stochastic modelling of chemical reactions. (arxiv.org)
  • Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. (cam.ac.uk)
  • Stochastic nature of gene transcription is widely known and its contribution to heterogeneity of cellular responses to various stimuli is well recognized. (actapress.com)
  • There are different techniques for the optimization of industrial processes that are widely used in industry, such as experimental design or surface response methodology to name a few. (igi-global.com)
  • It is widely believed that the establishment of morphogen gradients is a result of complex processes that involve diffusion and degradation of locally produced signaling molecules. (rice.edu)
  • The Chapman-Kolmogorov equation is introduced, transformed into a differential version, and used to classify the three major types of processes: (i) drift and (ii) diffusion with continuous sample paths, and (iii) jump processes which are essentially discontinuous. (springer.com)