Stochastic Processes: Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.Computer Simulation: Computer-based representation of physical systems and phenomena such as chemical processes.Models, Biological: 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.Models, Statistical: 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.Markov Chains: 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.Models, Genetic: 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.Biological Evolution: The process of cumulative change over successive generations through which organisms acquire their distinguishing morphological and physiological characteristics.Population Dynamics: The pattern of any process, or the interrelationship of phenomena, which affects growth or change within a population.Probability: The study of chance processes or the relative frequency characterizing a chance process.Ecosystem: 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)Models, Theoretical: 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.Monte Carlo Method: 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)Evolution, Molecular: The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Phylogeny: The relationships of groups of organisms as reflected by their genetic makeup.Selection, Genetic: Differential and non-random reproduction of different genotypes, operating to alter the gene frequencies within a population.Time Factors: Elements of limited time intervals, contributing to particular results or situations.Genetic Variation: Genotypic differences observed among individuals in a population.Mutation: 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.Kinetics: The rate dynamics in chemical or physical systems.

*  LIBOR and swap market models and measures (*)
Stochastic differential equations are derived for term structures of forward libor and swap rates, and shown to have a unique ... "Martingales and stochastic integrals in the theory of continuous trading," Stochastic Processes and their Applications, ... "Martingales and Stochastic Integrals in the Theory of Continous Trading," Discussion Papers 454, Northwestern University, ... Stochastic differential equations are derived for term structures of forward libor and swap rates, and shown to have a unique ...
*  Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control | Optimization | Discrete Mathematics |...
Appendix E. Markov Processes.. Answers to Selected Exercises.. References.. Frequently Used Notation. ... Stochastic search and optimization techniques are used in a vast number of areas, including aerospace, medicine, transportation ... Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the ... Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. James C. Spall ...
*  Applied Probability and Stochastic Processes | SpringerLink
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 ...
*  7.342 Systems Biology: Stochastic Processes and Biological Robustness
... 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, ...
*  EE 381J: Probability and Stochastic Processes I | Texas ECE
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. ...
*  Stochastic Processes : Poisson Process and Markov Chains
Suppose that shocks occur according to a Poisson process with rate A, 0. Also suppose that each shock independently causes the ... Stochastic Processes : Poisson Process and Markov Chains. Add. Remove. 1. Suppose that shocks occur according to a Poisson ... PS If you are not well versed in stochastic processes then please do not sign this problem out. ... 2. Let {N(t)},0 be a nonhomogeneous Poisson process with intensity function A(t) , 0. Then the mean value function 4u(t) is ...
*  Distances on Spaces of High-Dimensional Linear Stochastic Processes: A Survey | SpringerLink
In this paper we study the geometrization of certain spaces of stochastic processes. Our main motivation comes from the problem ... Martin, A.: A metric for ARMA processes. IEEE Trans. Signal Process. 48(4), 1164-70 (2000)CrossRefMATHMathSciNetGoogle Scholar ... Stochastic processes Pattern recognition Linear dynamical systems Extrinsic and intrinsic geometries Principal fiber bundle ... Rao, M.M.: Stochastic Processes: Inference Theory, vol. 508. Springer, New York (2000)Google Scholar ...
*  Stochastic Processes II | School of Mathematics | Georgia Institute of Technology | Atlanta, GA
At the level of Kulkarni, Modeling and Analysis of Stochastic Systems, and Karlin and Taylor, A First Course in Stochastic ...
*  Probability and mathematical genetics papers honour sir john kingman | Probability theory and stochastic processes | Cambridge...
7. Long-range dependence in a Cox process directed by an alternating renewal process D. J. Daley. 8. Kernel methods and minimum ... Diffusion processes and coalescent trees R. C. Griffiths and D. Spanó. 16. Three problems for the clairvoyant demon Geoffrey ... Coupling time distribution asymptotics for some couplings of the Lévy stochastic area W. S. Kendall. 20. Queueing with ... 3. Perfect simulation using dominated coupling from the past with application to area-interaction point processes and wavelet ...
*  Law (stochastic processes) - Wikipedia
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 ...
*  Using Observed Functional Data to Simulate a Stochastic Process via a Random Multiplicative Cascade Model | SpringerLink
... a method based on the definition of special Random Multiplicative Cascades to simulate the underlying stochastic process is ... An application to data from an industrial kneading process is considered.. Keywords. functional data stochastic process ... PREDA C., SAPORTA, G. (2005): PLS regression on a stochastic process. Computational Satistics and Data Analysis, 48:149-158. ... It will be considered a class S of stochastic processes whose realizations are real continuous piecewise linear functions with ...
*  Stochastic process - Wikipedia
The Wiener process is a member of some important families of stochastic processes, including Markov processes, Lévy processes ... then the stochastic process is referred to as a real-valued stochastic process or a process with continuous state space. If the ... to denote the stochastic process. Markov processes are stochastic processes, traditionally in discrete or continuous time, that ... Lévy processes, Gaussian processes, and random fields, renewal processes and branching processes. The study of stochastic ...
*  Semiclassical Analysis for Diffusions and Stochastic Processes by V.N. Kolokoltsov (Paperback, 2000) | eBay
Find great deals for Semiclassical Analysis for Diffusions and Stochastic Processes by V.N. Kolokoltsov (Paperback, 2000). Shop ... item 1 Semiclassical Analysis for Diffusions and Stochastic Processes by V.N. Kolokol't -Semiclassical Analysis for Diffusions ... item 2 Semiclassical Analysis for Diffusions and Stochastic Processes Kolokoltsov, Vass -Semiclassical Analysis for Diffusions ... especially stable jump-diffusions driven by stable Levy processes, (iii) complex stochastic Schrodinger equations which ...
*  Filtering problem (stochastic processes) - Wikipedia
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 ...
*  Infinitesimal generator (stochastic processes) - Wikipedia
In mathematics - specifically, in stochastic analysis - the infinitesimal generator of a stochastic process is a partial ... The Ornstein-Uhlenbeck process on R, which satisfies the stochastic differential equation dXt = θ (μ − Xt) dt + σ dBt, has ... The two-dimensional process Y satisfying d Y t = ( d t d B t ) , {\displaystyle \mathrm {d} Y_{t}={\mathrm {d} t \choose \ ... which satisfies the stochastic differential equation dXt = rXt dt + αXt dBt, has generator A f ( x ) = r x f ′ ( x ) + 1 2 α 2 ...
*  Smoothing problem (stochastic processes) - Wikipedia
Especially non-stochastic and non-Bayesian signal processing, without any hidden variables. 2. Estimation: The smoothing ... Filtering is causal but smoothing is batch processing of the same problem, namely, estimation of a time-series process based on ... especially non-stochastic signal processing, often a name of various types of convolution). These names are used in the context ... whereas smoothing is batch processing of the given data. Filtering is the estimation of a (hidden) time-series process based on ...
*  Backward Monte Carlo and other methods for 'reversing' stochastic processes - Edward L. Kaplan, Lawrence Radiation Laboratory,...
Backward Monte Carlo and other methods for 'reversing' stochastic processes. Edward L. Kaplan, Lawrence Radiation Laboratory, ... resulting reverse chronological order reverse process right eigenvectors S-th power solid angle Stochastic Processes Edward ... Backward Monte Carlo and other methods for 'reversing' stochastic processes. Authors. Edward L. Kaplan, Lawrence Radiation ... Carlo process Monte Carlo read neutron transport number of neutrons number of secondaries original matrix original process p(rn ...
*  Continuous stochastic process - Wikipedia
In probability theory, a continuous stochastic process is a type of stochastic process that may be said to be "continuous" as a ... this would be a continuous-time stochastic process, in parallel to a "discrete-time process". Given the possible confusion, ... 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. For ... It is implicit here that the index of the stochastic process is a continuous variable. Some authors define a "continuous ( ...
*  List of stochastic processes topics - Wikipedia
Markov chain Continuous-time Markov process Markov process Semi-Markov process Gauss-Markov processes: processes that are both ... bridge Brownian motion Chinese restaurant process CIR process Continuous stochastic process Cox process Dirichlet processes ... Stochastic control Stochastic differential equation Stochastic process Telegraph process Time series Wald's martingale Wiener ... See also Category:Stochastic processes Basic affine jump diffusion Bernoulli process: discrete-time processes with two possible ...
*  Stochastic Processes and their Applications - Wikipedia
"Stochastic Processes and their Applications Abstracting and Indexing". Stochastic Processes and their Applications. Elsevier. ... Stochastic Processes and their Applications is a monthly peer-reviewed scientific journal published by Elsevier for the ... "Stochastic Processes and their Applications". 2012 Journal Citation Reports. Web of Science (Science ed.). Thomson Reuters. ... The principal focus of this journal is theory and applications of stochastic processes. It was established in 1973. The journal ...
*  Download Stochastic Processes In Underwater Acoustics 1986
... download Stochastic Processes in Underwater processes on event ... download Stochastic vines on network studying social. The download Stochastic Processes in Underwater Acoustics affords star of ... The download Stochastic Processes of all artistic structures about is from the image that no existed image can participate them ... 4), which, despite their download Stochastic Processes in Underwater, those who are the tips in the scales cover still identify ...
*  Continuous-time stochastic process - Wikipedia
... a continuous-time stochastic process, or a continuous-space-time stochastic process is a stochastic process for which the index ... Continuous-time stochastic processes that are constructed from discrete-time processes via a waiting time distribution are ... An example of a continuous-time stochastic process for which sample paths are not continuous is a Poisson process. An example ... A more restricted class of processes are the continuous stochastic processes: here the term often (but not always) implies both ...
*  Stochastic processes and boundary value problems - Wikipedia
... the associated Dirichlet boundary value problem can be solved using an Itō process that solves an associated stochastic ... In mathematics, some boundary value problems can be solved using the methods of stochastic analysis. Perhaps the most ... Øksendal, Bernt K. (2003). Stochastic Differential Equations: An Introduction with Applications (Sixth ed.). Berlin: Springer. ... X can be taken to be the solution to the stochastic differential equation d X t = b ( X t ) d t + σ ( X t ) d B t , {\ ...
*  Stochastic process rare event sampling - Wikipedia
Stochastic Process Rare Event Sampling (SPRES) is a Rare Event Sampling method in computer simulation, designed specifically ... The process of branching requires that identical paths can be made to diverge from each other, such as by changing the seed in ... for non-equilibrium calculations, including those for which the rare-event rates are time-dependent (non-stationary process). ...
*  Appendix B A Few Math Facts - Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer...
For example, we use many properties of ... - Selection from Probability and Stochastic Processes: A Friendly Introduction for ... Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 3rd Edition by David J. ...

Doob decomposition theorem: In the theory of stochastic processes in discrete time, a part of the mathematical theory of probability, the Doob decomposition theorem gives a unique decomposition of every adapted and integrable stochastic process as the sum of a martingale and a predictable process (or "drift") starting at zero. The theorem was proved by and is named for Joseph L.Interval boundary element method: Interval boundary element method is classical boundary element method with the interval parameters.
Matrix model: == Mathematics and physics ==Inverse probability weighting: Inverse probability weighting is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.Vladimir Andreevich Markov: Vladimir Andreevich Markov (; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov. He died of tuberculosis at the age of 25.Matrix population models: Population models are used in population ecology to model the dynamics of wildlife or human populations. Matrix population models are a specific type of population model that uses matrix algebra.Negative probability: The probability of the outcome of an experiment is never negative, but quasiprobability distributions can be defined that allow a negative probability for some events. These distributions may apply to unobservable events or conditional probabilities.EcosystemVon Neumann regular ring: In mathematics, a von Neumann regular ring is a ring R such that for every a in R there exists an x in R such that . To avoid the possible confusion with the regular rings and regular local rings of commutative algebra (which are unrelated notions), von Neumann regular rings are also called absolutely flat rings, because these rings are characterized by the fact that every left module is flat.Monte Carlo methods for option pricing: In mathematical finance, a Monte Carlo option model uses Monte Carlo methods Although the term 'Monte Carlo method' was coined by Stanislaw Ulam in the 1940s, some trace such methods to the 18th century French naturalist Buffon, and a question he asked about the results of dropping a needle randomly on a striped floor or table. See Buffon's needle.Molecular evolution: Molecular evolution is a change in the sequence composition of cellular molecules such as DNA, RNA, and proteins across generations. The field of molecular evolution uses principles of evolutionary biology and population genetics to explain patterns in these changes.Clonal Selection Algorithm: In artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation.Branching order of bacterial phyla (Gupta, 2001): There are several models of the Branching order of bacterial phyla, one of these was proposed in 2001 by Gupta based on conserved indels or protein, termed "protein signatures", an alternative approach to molecular phylogeny. Some problematic exceptions and conflicts are present to these conserved indels, however, they are in agreement with several groupings of classes and phyla.Selection (relational algebra): In relational algebra, a selection (sometimes called a restriction to avoid confusion with SQL's use of SELECT) is a unary operation written asTemporal analysis of products: Temporal Analysis of Products (TAP), (TAP-2), (TAP-3) is an experimental technique for studyingGenetic variation: right|thumbSilent mutation: Silent mutations are mutations in DNA that do not significantly alter the phenotype of the organism in which they occur. Silent mutations can occur in non-coding regions (outside of genes or within introns), or they may occur within exons.Burst kinetics: Burst kinetics is a form of enzyme kinetics that refers to an initial high velocity of enzymatic turnover when adding enzyme to substrate. This initial period of high velocity product formation is referred to as the "Burst Phase".

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

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)

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

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)

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

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)

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

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)

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

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 for the library of HMMs of structural families used in this paper. FORESST web server at for a more extensive library of HMMs (work in progress). CONTACT:;;  (+info)

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

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)

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

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)

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

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)

  • numerical
  • Historically, the random variables were associated with or indexed by a set of numbers, usually viewed as points in time, giving the interpretation of a stochastic process representing numerical values of some system randomly changing over time, such as the growth of a bacterial population, an electrical current fluctuating due to thermal noise, or the movement of a gas molecule. (
  • calculus
  • The Doob-Meyer decomposition theorem is a theorem in stochastic calculus stating the conditions under which a submartingale may be decomposed in a unique way as the sum of a martingale and an increasing predictable process. (
  • displaystyle
  • The Ornstein-Uhlenbeck process on R, which satisfies the stochastic differential equation dXt = θ (μ − Xt) dt + σ dBt, has generator A f ( x ) = θ ( μ − x ) f ′ ( x ) + σ 2 2 f ″ ( x ) . {\displaystyle Af(x)=\theta (\mu -x)f'(x)+{\frac {\sigma ^{2}}{2}}f''(x). (
  • Similarly, the graph of the Ornstein-Uhlenbeck process has generator A f ( t , x ) = ∂ f ∂ t ( t , x ) + θ ( μ − x ) ∂ f ∂ x ( t , x ) + σ 2 2 ∂ 2 f ∂ x 2 ( t , x ) . {\displaystyle Af(t,x)={\frac {\partial f}{\partial t}}(t,x)+\theta (\mu -x){\frac {\partial f}{\partial x}}(t,x)+{\frac {\sigma ^{2}}{2}}{\frac {\partial ^{2}f}{\partial x^{2}}}(t,x). (
  • displaystyle {\lim _{y\to x}u(y)}=g(x),&x\in \partial D.\end{cases}}\quad {\mbox{(P1)}}} The idea of the stochastic method for solving this problem is as follows. (
  • Let Z {\displaystyle Z} be a cadlag submartingale of class D. Then there exists a unique, increasing, predictable process A {\displaystyle A} with A 0 = 0 {\displaystyle A_{0}=0} such that M t = Z t − A t {\displaystyle M_{t}=Z_{t}-A_{t}} is a uniformly integrable martingale. (
  • realization
  • A stochastic process can have many outcomes, due to its randomness, and a single outcome of a stochastic process is called, among other names, a sample function or realization. (
  • Performing the random experiment means choosing an outcome ω Ω at random according to the probability measure P. Definition The function (defined on the index set T and taking values in R) t X t (ω) is called the sample path (or the realization, or the trajectory) of the stochastic process X corresponding to the outcome ω. (
  • A realization of this process is shown in Figure 1, left Figure 1. (
  • continuity
  • Continuity is a nice property for (the sample paths of) a process to have, since it implies that they are well-behaved in some sense, and, therefore, much easier to analyze. (
  • Functional
  • Considering functional data and an associated binary response, a method based on the definition of special Random Multiplicative Cascades to simulate the underlying stochastic process is proposed. (
  • telegraph
  • Even worse, even if A is an event, P(A) can be strictly positive even if P(At) = 0 for every t ∈ T. This is the case, for example, with the telegraph process. (
  • chapter
  • The old chapter on queues has been expanded and broken into two new chapters: one for simple queuing processes and one for queuing networks. (
  • define
  • Define X t = ξ 0 + ξ 1 t, t R. The process {X t : t R} might be called a random line because the sample paths t X t (ω) are linear functions. (
  • For example, it is common to define a Markov chain as a Markov process in either discrete or continuous time with a countable state space (thus regardless of the nature of time), but it is also common to define a Markov chain as having discrete time in either countable or continuous state space (thus regardless of the state space). (
  • decomposition
  • b) FindP{NrrnT=t}. (c) Describe how the results from part (b) could have alternatively been determined by considering an appropriate decomposition of the original Poisson process. (
  • densities
  • 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. (
  • applications
  • Applications and the study of phenomena have in turn inspired the proposal of new stochastic processes. (
  • Markov chains have many applications as statistical models of real-world processes, such as studying cruise control systems in motor vehicles, queues or lines of customers arriving at an airport, exchange rates of currencies, storage systems such as dams, and population growths of certain animal species. (
  • systems
  • In this seminar, we will discuss some of the main themes that have arisen in the field of systems biology, including the concepts of robustness, stochastic cell-to-cell variability, and the evolution of molecular interactions within complex networks. (
  • In the second part, we focus on the space of processes generated by (stochastic) linear dynamical systems (LDSs) of fixed size and order, for which we recently introduced a class of group action induced distances called the alignment distances. (
  • random variables
  • 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. (
  • Any collection of random variables X = {X t : t T } defined on (Ω, F, P) is called a stochastic process with index set T. So, to every t T corresponds some random variable X t : Ω R, ω X t (ω). (