Chemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPhenomena and ProcessesInformation ScienceHealth Care
Monte Carlo MethodMarkov ChainsComputer SimulationBayes TheoremRadiometryAlgorithmsModels, StatisticalPhotonsPhantoms, ImagingRadiotherapy Planning, Computer-AssistedModels, GeneticModels, BiologicalScattering, RadiationLikelihood FunctionsModels, TheoreticalSoftwareRadiotherapy DosageModels, ChemicalStochastic ProcessesReproducibility of ResultsThermodynamicsProbabilityModels, MolecularProtein ConformationPhylogenyRadiation DosageGenetic MarkersEvolution, MolecularChromosome MappingTime FactorsKineticsFourier AnalysisHot TemperatureElectron TransportTemperature
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)
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.
Computer Simulation
Computer-based representation of physical systems and phenomena such as chemical processes.
Bayes Theorem
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.
Radiometry
The measurement of radiation by photography, as in x-ray film and film badge, by Geiger-Mueller tube, and by SCINTILLATION COUNTING.
Algorithms
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
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.
Photons
Discrete concentrations of energy, apparently massless elementary particles, that move at the speed of light. They are the unit or quantum of electromagnetic radiation. Photons are emitted when electrons move from one energy state to another. (From Hawley's Condensed Chemical Dictionary, 11th ed)
Phantoms, Imaging
Devices or objects in various imaging techniques used to visualize or enhance visualization by simulating conditions encountered in the procedure. Phantoms are used very often in procedures employing or measuring x-irradiation or radioactive material to evaluate performance. Phantoms often have properties similar to human tissue. Water demonstrates absorbing properties similar to normal tissue, hence water-filled phantoms are used to map radiation levels. Phantoms are used also as teaching aids to simulate real conditions with x-ray or ultrasonic machines. (From Iturralde, Dictionary and Handbook of Nuclear Medicine and Clinical Imaging, 1990)
Radiotherapy Planning, Computer-Assisted
Computer-assisted mathematical calculations of beam angles, intensities of radiation, and duration of irradiation in radiotherapy.
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.
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.
Scattering, Radiation
The diversion of RADIATION (thermal, electromagnetic, or nuclear) from its original path as a result of interactions or collisions with atoms, molecules, or larger particles in the atmosphere or other media. (McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)
Likelihood Functions
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
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.
Software
Sequential operating programs and data which instruct the functioning of a digital computer.
Radiotherapy Dosage
The total amount of radiation absorbed by tissues as a result of radiotherapy.
Models, Chemical
Theoretical representations that simulate the behavior or activity of chemical processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.
Stochastic Processes
Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.
Reproducibility of Results
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
Thermodynamics
A rigorously mathematical analysis of energy relationships (heat, work, temperature, and equilibrium). It describes systems whose states are determined by thermal parameters, such as temperature, in addition to mechanical and electromagnetic parameters. (From Hawley's Condensed Chemical Dictionary, 12th ed)
Probability
The study of chance processes or the relative frequency characterizing a chance process.
Models, Molecular
Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.
Protein Conformation
The characteristic 3-dimensional shape of a protein, including the secondary, supersecondary (motifs), tertiary (domains) and quaternary structure of the peptide chain. PROTEIN STRUCTURE, QUATERNARY describes the conformation assumed by multimeric proteins (aggregates of more than one polypeptide chain).
Phylogeny
The relationships of groups of organisms as reflected by their genetic makeup.
Radiation Dosage
The amount of radiation energy that is deposited in a unit mass of material, such as tissues of plants or animal. In RADIOTHERAPY, radiation dosage is expressed in gray units (Gy). In RADIOLOGIC HEALTH, the dosage is expressed by the product of absorbed dose (Gy) and quality factor (a function of linear energy transfer), and is called radiation dose equivalent in sievert units (Sv).
Genetic Markers
A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.
Evolution, Molecular
The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.
Chromosome Mapping
Any method used for determining the location of and relative distances between genes on a chromosome.
Time Factors
Elements of limited time intervals, contributing to particular results or situations.
Kinetics
The rate dynamics in chemical or physical systems.
Fourier Analysis
Analysis based on the mathematical function first formulated by Jean-Baptiste-Joseph Fourier in 1807. The function, known as the Fourier transform, describes the sinusoidal pattern of any fluctuating pattern in the physical world in terms of its amplitude and its phase. It has broad applications in biomedicine, e.g., analysis of the x-ray crystallography data pivotal in identifying the double helical nature of DNA and in analysis of other molecules, including viruses, and the modified back-projection algorithm universally used in computerized tomography imaging, etc. (From Segen, The Dictionary of Modern Medicine, 1992)
Hot Temperature
Presence of warmth or heat or a temperature notably higher than an accustomed norm.
Electron Transport
The process by which ELECTRONS are transported from a reduced substrate to molecular OXYGEN. (From Bennington, Saunders Dictionary and Encyclopedia of Laboratory Medicine and Technology, 1984, p270)
Temperature
The property of objects that determines the direction of heat flow when they are placed in direct thermal contact. The temperature is the energy of microscopic motions (vibrational and translational) of the particles of atoms.

Simplified methods for pKa and acid pH-dependent stability estimation in proteins: removing dielectric and counterion boundaries. (1/4146)

Much computational research aimed at understanding ionizable group interactions in proteins has focused on numerical solutions of the Poisson-Boltzmann (PB) equation, incorporating protein exclusion zones for solvent and counterions in a continuum model. Poor agreement with measured pKas and pH-dependent stabilities for a (protein, solvent) relative dielectric boundary of (4,80) has lead to the adoption of an intermediate (20,80) boundary. It is now shown that a simple Debye-Huckel (DH) calculation, removing both the low dielectric and counterion exclusion regions associated with protein, is equally effective in general pKa calculations. However, a broad-based discrepancy to measured pH-dependent stabilities is maintained in the absence of ionizable group interactions in the unfolded state. A simple model is introduced for these interactions, with a significantly improved match to experiment that suggests a potential utility in predicting and analyzing the acid pH-dependence of protein stability. The methods are applied to the relative pH-dependent stabilities of the pore-forming domains of colicins A and N. The results relate generally to the well-known preponderance of surface ionizable groups with solvent-mediated interactions. Although numerical PB solutions do not currently have a significant advantage for overall pKa estimations, development based on consideration of microscopic solvation energetics in tandem with the continuum model could combine the large deltapKas of a subset of ionizable groups with the overall robustness of the DH model.  (+info)

Temporal and multiple quantitative trait loci analyses of resistance to bacterial wilt in tomato permit the resolution of linked loci. (2/4146)

Ralstonia solanacearum is a soil-borne bacterium that causes the serious disease known as bacterial wilt in many plant species. In tomato, several QTL controlling resistance have been found, but in different studies, markers spanning a large region of chromosome 6 showed strong association with the resistance. By using two different approaches to analyze the data from a field test F3 population, we show that at least two separate loci approximately 30 cM apart on this chromosome are most likely involved in the resistance. First, a temporal analysis of the progression of symptoms reveals a distal locus early in the development of the disease. As the disease progresses, the maximum LOD peak observed shifts toward the proximal end of the chromosome, obscuring the distal locus. Second, although classical interval mapping could only detect the presence of one locus, a statistical "two-QTL model" test, specifically adapted for the resolution of linked QTL, strongly supported the hypothesis for the presence of two loci. These results are discussed in the context of current molecular knowledge about disease resistance genes on chromosome 6 and observations made by tomato breeders during the production of bacterial wilt-resistant varieties.  (+info)

Local control models of cardiac excitation-contraction coupling. A possible role for allosteric interactions between ryanodine receptors. (3/4146)

In cardiac muscle, release of activator calcium from the sarcoplasmic reticulum occurs by calcium- induced calcium release through ryanodine receptors (RyRs), which are clustered in a dense, regular, two-dimensional lattice array at the diad junction. We simulated numerically the stochastic dynamics of RyRs and L-type sarcolemmal calcium channels interacting via calcium nano-domains in the junctional cleft. Four putative RyR gating schemes based on single-channel measurements in lipid bilayers all failed to give stable excitation-contraction coupling, due either to insufficiently strong inactivation to terminate locally regenerative calcium-induced calcium release or insufficient cooperativity to discriminate against RyR activation by background calcium. If the ryanodine receptor was represented, instead, by a phenomenological four-state gating scheme, with channel opening resulting from simultaneous binding of two Ca2+ ions, and either calcium-dependent or activation-linked inactivation, the simulations gave a good semiquantitative accounting for the macroscopic features of excitation-contraction coupling. It was possible to restore stability to a model based on a bilayer-derived gating scheme, by introducing allosteric interactions between nearest-neighbor RyRs so as to stabilize the inactivated state and produce cooperativity among calcium binding sites on different RyRs. Such allosteric coupling between RyRs may be a function of the foot process and lattice array, explaining their conservation during evolution.  (+info)

Quantal amplitude and quantal variance of strontium-induced asynchronous EPSCs in rat dentate granule neurons. (4/4146)

1. Excitatory postsynaptic currents (EPSCs) were recorded from granule cells of the dentate gyrus in acute slices of 17- to 21-day-old rats (22-25 C) using tissue cuts and minimal extracellular stimulation to selectively activate a small number of synaptic contacts. 2. Adding millimolar Sr2+ to the external solution produced asynchronous EPSCs (aEPSCs) lasting for several hundred milliseconds after the stimulus. Minimally stimulated aEPSCs resembled miniature EPSCs (mEPSCs) recorded in the same cell but differed from them in ways expected from the greater range of dendritic filtering experienced by mEPSCs. aEPSCs had the same stimulus threshold as the synchronous EPSCs (sEPSCs) that followed the stimulus with a brief latency. aEPSCs following stimulation of distal inputs had a slower mean rise time than those following stimulation of proximal inputs. These results suggest that aEPSCs arose from the same synapses that generated sEPSCs. 3. Proximally elicited aEPSCs had a mean amplitude of 6.7 +/- 2.2 pA (+/- s.d., n = 23 cells) at -70 mV and an amplitude coefficient of variation of 0. 46 +/- 0.08. 4. The amplitude distributions of sEPSCs never exhibited distinct peaks. 5. Monte Carlo modelling of the shapes of aEPSC amplitude distributions indicated that our data were best explained by an intrasite model of quantal variance. 6. It is concluded that Sr2+-evoked aEPSCs are uniquantal events arising at synaptic terminals that were recently invaded by an action potential, and so provide direct information about the quantal amplitude and quantal variance at those terminals. The large quantal variance obscures quantization of the amplitudes of evoked sEPSCs at this class of excitatory synapse.  (+info)

Bayesian inference on biopolymer models. (5/4146)

MOTIVATION: Most existing bioinformatics methods are limited to making point estimates of one variable, e.g. the optimal alignment, with fixed input values for all other variables, e.g. gap penalties and scoring matrices. While the requirement to specify parameters remains one of the more vexing issues in bioinformatics, it is a reflection of a larger issue: the need to broaden the view on statistical inference in bioinformatics. RESULTS: The assignment of probabilities for all possible values of all unknown variables in a problem in the form of a posterior distribution is the goal of Bayesian inference. Here we show how this goal can be achieved for most bioinformatics methods that use dynamic programming. Specifically, a tutorial style description of a Bayesian inference procedure for segmentation of a sequence based on the heterogeneity in its composition is given. In addition, full Bayesian inference algorithms for sequence alignment are described. AVAILABILITY: Software and a set of transparencies for a tutorial describing these ideas are available at http://www.wadsworth.org/res&res/bioinfo/  (+info)

A hierarchical approach to protein molecular evolution. (6/4146)

Biological diversity has evolved despite the essentially infinite complexity of protein sequence space. We present a hierarchical approach to the efficient searching of this space and quantify the evolutionary potential of our approach with Monte Carlo simulations. These simulations demonstrate that nonhomologous juxtaposition of encoded structure is the rate-limiting step in the production of new tertiary protein folds. Nonhomologous "swapping" of low-energy secondary structures increased the binding constant of a simulated protein by approximately 10(7) relative to base substitution alone. Applications of our approach include the generation of new protein folds and modeling the molecular evolution of disease.  (+info)

The topomer-sampling model of protein folding. (7/4146)

Clearly, a protein cannot sample all of its conformations (e.g., approximately 3(100) approximately 10(48) for a 100 residue protein) on an in vivo folding timescale (<1 s). To investigate how the conformational dynamics of a protein can accommodate subsecond folding time scales, we introduce the concept of the native topomer, which is the set of all structures similar to the native structure (obtainable from the native structure through local backbone coordinate transformations that do not disrupt the covalent bonding of the peptide backbone). We have developed a computational procedure for estimating the number of distinct topomers required to span all conformations (compact and semicompact) for a polypeptide of a given length. For 100 residues, we find approximately 3 x 10(7) distinct topomers. Based on the distance calculated between different topomers, we estimate that a 100-residue polypeptide diffusively samples one topomer every approximately 3 ns. Hence, a 100-residue protein can find its native topomer by random sampling in just approximately 100 ms. These results suggest that subsecond folding of modest-sized, single-domain proteins can be accomplished by a two-stage process of (i) topomer diffusion: random, diffusive sampling of the 3 x 10(7) distinct topomers to find the native topomer ( approximately 0.1 s), followed by (ii) intratopomer ordering: nonrandom, local conformational rearrangements within the native topomer to settle into the precise native state.  (+info)

Testing the fit of a quantal model of neurotransmission. (8/4146)

Many studies of synaptic transmission have assumed a parametric model to estimate the mean quantal content and size or the effect upon them of manipulations such as the induction of long-term potentiation. Classical tests of fit usually assume that model parameters have been selected independently of the data. Therefore, their use is problematic after parameters have been estimated. We hypothesized that Monte Carlo (MC) simulations of a quantal model could provide a table of parameter-independent critical values with which to test the fit after parameter estimation, emulating Lilliefors's tests. However, when we tested this hypothesis within a conventional quantal model, the empirical distributions of two conventional goodness-of-fit statistics were affected by the values of the quantal parameters, falsifying the hypothesis. Notably, the tests' critical values increased when the combined variances of the noise and quantal-size distributions were reduced, increasing the distinctness of quantal peaks. Our results support two conclusions. First, tests that use a predetermined critical value to assess the fit of a quantal model after parameter estimation may operate at a differing unknown level of significance for each experiment. Second, a MC test enables a valid assessment of the fit of a quantal model after parameter estimation.  (+info)

Monte Carlo simulation of xenon adsorption with dealumination of an idealized mordenite structure<...
TY - JOUR. T1 - Monte Carlo simulation of xenon adsorption with dealumination of an idealized mordenite structure. AU - Nivarthi, Sriram S.. AU - Van Tassel, Paul R.. AU - Davis, H. Ted. AU - McCormick, Alon V.. N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.. PY - 1995/1. Y1 - 1995/1. N2 - Grand canonical Monte Carlo simulations were used to study the effect of dealumination on adsorption of xenon in an idealized structural model of Na-mordenite. Simulations were carried out at Si/Al ratios of 5, 11, and ∞, chosen to represent prototype cation occupancies. A recent experimental study of xenon adsorption in mordenites with different Si/Al ratios found unexpected nonmonotonic trends. In disagreement with experiment, the isotherms of our idealized zeolite changed monotonically with the degree of dealumination. This trend supports recent observations that significant structural chages result from dealumination.. AB - Grand canonical Monte Carlo simulations were used to study ...
https://minnesota-staging.pure.elsevier.com/en/publications/monte-carlo-simulation-of-xenon-adsorption-with-dealumination-of-
Performance of Hamiltonian Monte Carlo and No-U-Turn Sampler for estimating genetic parameters and breeding values | Genetics...Performance of Hamiltonian Monte Carlo and No-U-Turn Sampler for estimating genetic parameters and breeding values | Genetics...
Hamiltonian Monte Carlo is one of the algorithms of the Markov chain Monte Carlo method that uses Hamiltonian dynamics to propose samples that follow a target distribution. The method can avoid the random walk behavior to achieve a more effective and consistent exploration of the probability space and sensitivity to correlated parameters, which are shortcomings that plague many Markov chain Monte Carlo methods. However, the performance of Hamiltonian Monte Carlo is highly sensitive to two hyperparameters. The No-U-Turn Sampler, an extension of Hamiltonian Monte Carlo, was recently introduced to automate the tuning of these hyperparameters. Thus, this study compared the performances of Gibbs sampling, Hamiltonian Monte Carlo, and the No-U-Turn Sampler for estimating genetic parameters and breeding values as well as sampling qualities in both simulated and real pig data. For all datasets, we used a pedigree-based univariate linear mixed model. For all datasets, the No-U-Turn Sampler and Gibbs sampling
https://gsejournal.biomedcentral.com/articles/10.1186/s12711-019-0515-1
Self-evolving atomistic kinetic Monte Carlo simulations of defects in materials (Journal Article) | DOE PAGESSelf-evolving atomistic kinetic Monte Carlo simulations of defects in materials (Journal Article) | DOE PAGES
The recent development of on-the-fly atomistic kinetic Monte Carlo methods has led to an increased amount attention on the methods and their corresponding capabilities and applications. In this review, the framework and current status of Self-Evolving Atomistic Kinetic Monte Carlo (SEAKMC) are discussed. SEAKMC particularly focuses on defect interaction and evolution with atomistic details without assuming potential defect migration/interaction mechanisms and energies. The strength and limitation of using an active volume, the key concept introduced in SEAKMC, are discussed. Potential criteria for characterizing an active volume are discussed and the influence of active volume size on saddle point energies is illustrated. A procedure starting with a small active volume followed by larger active volumes was found to possess higher efficiency. Applications of SEAKMC, ranging from point defect diffusion, to complex interstitial cluster evolution, to helium interaction with tungsten surfaces, are ...
https://www.osti.gov/pages/biblio/1185974-self-evolving-atomistic-kinetic-monte-carlo-simulations-defects-materials
Markov Chain Monte Carlo Method without Detailed Balance - Condensed Matter > Statistical Mechanics - pdf...
Markov Chain Monte Carlo Method without Detailed Balance - Condensed Matter > Statistical Mechanics. . 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.
http://libros.duhnnae.com/2017/sep/15052586771-Markov-Chain-Monte-Carlo-Method-without-Detailed-Balance-Condensed-Matter-Statistical-Mechanics.php
Monte Carlo simulation of the transient response of single photon absorption in X-ray pixel detectorsMonte Carlo simulation of the transient response of single photon absorption in X-ray pixel detectors
A Monte Carlo study of the transient response of single photon absorption in X-ray pixel detectors is presented. The simulation results have been combined with Monte Carlo simulation of the X-ray photon transport and absorption, and used to estimate the image properties of a detector system, including the pixel array and readout electronics. The study includes several different simulation challenges, such as full band Monte Carlo simulation of charge transport in large devices (300 mu m * 100 mu m), modelling of three-dimensional electrostatic effects using cylindrical coordinates, Monte Carlo simulation of photon transport and absorption, and a system level Monte Carlo simulation of the entire pixel detector and readout. ...
http://miun.diva-portal.org/smash/record.jsf?pid=diva2:26707
Evaluation based on Monte Carlo simulation of lifetime attributable risk of cancer after neck X-ray radiography - Fingerprint ...
Fingerprint Dive into the research topics of Evaluation based on Monte Carlo simulation of lifetime attributable risk of cancer after neck X-ray radiography. Together they form a unique fingerprint. ...
https://koreauniv.pure.elsevier.com/en/publications/evaluation-based-on-monte-carlo-simulation-of-lifetime-attributab/fingerprints/
talks.cam : From calcium imaging to spikes, using sequential Monte Carlo methodstalks.cam : From calcium imaging to spikes, using sequential Monte Carlo methods
If you have a question about this talk, please contact Rachel Fogg.. Great technological and experimental advances have recently facilitated the imaging neural activity both in live animals. We describe a sequential Monte Carlo (SMC) expectation maximization algorithm that both infers the posterior distributions of the hidden states, and finds the maximum likelihood estimates of the parameters. Using such an approach enables us to (i) incorporate errorbars on the estimate of the hidden states, (ii) allow for nonlinearities in the observation and transition distributions, and (iii) consider Markov priors governing neural activity. This strategy works in real time for each observable neuron. We show how this method can condition the inferred spike trains on external stimuli, and achieve superresolution, i.e., infer not just whether a spike occurred within a stimulus frame, but when within that frame. Furthermore, our model has a relatively small number of parameters, and each of the parameters may ...
https://talks.cam.ac.uk/talk/index/13808
First Passage Probability Estimation of Wind Turbines by Markov Chain Monte Carlo - Danish National Research Database-Den...First Passage Probability Estimation of Wind Turbines by Markov Chain Monte Carlo - Danish National Research Database-Den...
Markov Chain Monte Carlo simulation has received considerable attention within the past decade as reportedly one of the most powerful techniques for the first passage probability estimation of dynamic systems. A very popular method in this direction capable of estimating probability of rare events with low computation cost is the subset simulation (SS). The idea of the method is to break a rare event into a sequence of more probable events which are easy to be estimated based on the conditional simulation techniques. Recently, two algorithms have been proposed in order to increase the efficiency of the method by modifying the conditional sampler. In this paper, applicability of the original SS is compared to the recently introduced modifications of the method on a wind turbine model. The model incorporates a PID pitch controller which aims at keeping the rotational speed of the wind turbine rotor equal to its nominal value. Finally Monte Carlo simulations are performed which allow assessment of ...
http://www.forskningsdatabasen.dk/catalog/242595453
NOPR: Estimation of uncertainty of effective area of a pneumatic pressure reference standard using Monte Carlo methodNOPR: Estimation of uncertainty of effective area of a pneumatic pressure reference standard using Monte Carlo method
The current paper presents a comparative investigation of the experimental as well as simulated evaluation of effective area and the associated uncertainties, of a pneumatic pressure reference standard (NPLI-4) of CSIR-National Physical Laboratory, India, (NPLI). The experimental evaluation has been compared to the simulated estimation of the effective area obtained through Monte Carlo method (MCM). The Monte Carlo method has been applied by taking fixed number of trials (FMCM) and also by trials chosen adaptively (AMCM). The measurement uncertainties have been calculated using the conventional method, i.e., law of propagation of uncertainty (LPU) as well as MCM. Experimentally, the NPLI-4 has cross-floated against our newly established pneumatic primary pressure standard (NPLI-P10), which is a large diameter piston gauge. An excellent agreement in effective area and measurement uncertainty has been observed between these approaches ...
http://nopr.niscair.res.in/handle/123456789/39056
Dose Assessment of Eye and Its Components in Proton Therapy by Monte Carlo MethodDose Assessment of Eye and Its Components in Proton Therapy by Monte Carlo Method
Introduction Proton therapy is used to treat malignant tumors such as melanoma inside the eye. Proton particles are adjusted according to various parameters such as tumor size and position and patients distance from the proton source. The purpose of this study was to assess absorbed doses in eyes and various tumors found in the area of sclera and choroid and the adjacent tissues in radiotherapy while changing most important proton therapy parameters such as moderators thickness (1.5-1.9 cm), exposure radius (0.5-0.8 cm), and proton energy beam (53.5-65 MeV). Materials and Methods A proton therapy system of Laboratori Nazionali del Sud-INFNwas simulated by Monte Carlo method. Moreover, the eye and its components were simulated using concentric spheres. To obtain a more accurate results, real density of eye components such as cornea and lens, were applied for simulation. Then, the absorbed dose of eye and eye tumor, in choroid and sclera areas, were calculated by Monte Carlo method. Results The absorbed
http://ijmp.mums.ac.ir/article_2631.html
Monte Carlo Method to Solve Diffusion Equation and Error Analysis | Journal of Nepal Mathematical SocietyMonte Carlo Method to Solve Diffusion Equation and Error Analysis | Journal of Nepal Mathematical Society
Three different mathematical approaches for the evolution of diffusion equation are presented. The evolution process of the diffusion equation is explained by principle of conservation law, probability distribution procedure, and finally though stochastic differential equation (SDE) driven by Brownian motion. The Monte Carlo method is discussed to solve the diffusion equation by generating the normally distributed random numbers and the root mean square error is derived for the Monte Carlo method. The numerical solutions are computed for 1-dimensional diffusion equation and results are compared with exact solution. Finally, theoretical root mean square error is compared with the maximum error and the L2-error by increasing the number of simulated points.. ...
https://www.nepjol.info/index.php/jnms/article/view/37113
Download Introducing Monte Carlo Methods With R
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http://thesa.com/ebooks/download-introducing-monte-carlo-methods-with-r.htm
Particle flow for sequential Monte Carlo implementation of probability hypothesis density - Surrey Research Insight Open AccessParticle flow for sequential Monte Carlo implementation of probability hypothesis density - Surrey Research Insight Open Access
Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the full multi-target posterior. Recently, the particle flow filter based on the log homotopy provides a new way for state estimation. In this paper, we propose a novel sequential Monte Carlo (SMC) implementation for the PHD filter assisted by the particle flow (PF), which is called PF-SMCPHD filter. Experimental results show that our proposed filter has higher accuracy than the SMC-PHD filter and is computationally cheaper than the Gaussian mixture PHD (GM-PHD) filter.. ...
http://epubs.surrey.ac.uk/813537/
Monte Carlo algorithm - Simple English Wikipedia, the free encyclopediaMonte Carlo algorithm - Simple English Wikipedia, the free encyclopedia
It is a computation process that uses random numbers to produce an outcome(s). Instead of having fixed inputs, probability distributions are assigned to some or all of the inputs. This will generate a probability distribution for the output after the simulation is run.. For example, a Monte Carlo algorithm can be used to estimate the value of π. The amount of area within a quarter-circle of radius 1 depends on the value of π. The probability that a randomly-chosen point will lie in that quarter-circle depends on the area of the circle. If points are placed randomly in a square with sides of length 1, the percentage of points that fall within a quarter-circle of radius 1 will depend on the value of π. A Monte Carlo algorithm would randomly place points in the square and use the percentage of points falling inside of the circle to estimate the value of π.This is an effective way for making approximations.. In modern communication systems, the quality of information exchange is determined by ...
https://simple.wikipedia.org/wiki/Monte_Carlo_algorithm
Adsorption equilibrium of polar/non-polar mixtures on MCM-41: Experiments and Monte Carlo simulation<...
TY - JOUR. T1 - Adsorption equilibrium of polar/non-polar mixtures on MCM-41. T2 - Experiments and Monte Carlo simulation. AU - Yun., J. H.. AU - He, Y.. AU - Otero, M.. AU - Düren, T.. AU - Seaton, N. A.. PY - 2002/12/1. Y1 - 2002/12/1. N2 - We have studied the adsorption of mixtures of polar and non-polar gases in MCM-41. MCM-41 is a suitable model on which to test our understanding of adsorption at the molecular level, and to evaluate methods for the prediction of multicomponent adsorption equilibrium. The adsorption of mixtures of methane, ethane and carbon dioxide (and the corresponding pure gases) was studied using experiment, grand canonical Monte Carlo (GCMC) simulation, and ideal adsorbed solution theory (IAST). Both GCMC and IAST work very well for the adsorption of the methane/ethane mixture in MCM-41. IAST gives good predictions for ethane/carbon dioxide mixture adsorption equilibrium at low and moderate pressures, and exhibits some deviations at relatively high pressures.. AB - We ...
https://researchportal.bath.ac.uk/en/publications/adsorption-equilibrium-of-polarnon-polar-mixtures-on-mcm-41-exper
Monte Carlo method - WikipediaMonte Carlo method - Wikipedia
Being secret, the work of von Neumann and Ulam required a code name.[14] A colleague of von Neumann and Ulam, Nicholas Metropolis, suggested using the name Monte Carlo, which refers to the Monte Carlo Casino in Monaco where Ulams uncle would borrow money from relatives to gamble.[12] Using lists of truly random random numbers was extremely slow, but von Neumann developed a way to calculate pseudorandom numbers, using the middle-square method. Though this method has been criticized as crude, von Neumann was aware of this: he justified it as being faster than any other method at his disposal, and also noted that when it went awry it did so obviously, unlike methods that could be subtly incorrect. Monte Carlo methods were central to the simulations required for the Manhattan Project, though severely limited by the computational tools at the time. In the 1950s they were used at Los Alamos for early work relating to the development of the hydrogen bomb, and became popularized in the fields of ...
https://en.m.wikipedia.org/wiki/Monte_Carlo_simulation
The Price is Right : Project valuation for Project Portfolio Management using Markov Chain Monte Carlo SimulationThe Price is Right : Project valuation for Project Portfolio Management using Markov Chain Monte Carlo Simulation
A common managerial problem in the project-based organization is the problem of resource allocation. In practice this problem is addressed by applying project portfolio management. In this study we examine project portfolio management in a consultancy firm by applying a mathematical model. The information produced by this model could enable rational decision making and thus improve the economic resilience and reduce internal uncertainty in the firm so that it may live long and prosper. The proposed model is based on a Markov process that represents the projects in the firm. The parameters are estimated by Maximum Likelihood and the results are estimated through Monte Carlo Simulation.. This study initially shows that it is possible to model the project portfolio as a Markov process. This was supported by the conducted literature review and illustrated by the presented model. Furthermore, we conclude that the value of accepting a project is dependent on the current state of the firm in terms of ...
http://kth.diva-portal.org/smash/record.jsf?pid=diva2:942707
Implementation and Analysis of an Adaptive Multilevel Monte Carlo AlgorithmImplementation and Analysis of an Adaptive Multilevel Monte Carlo Algorithm
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 ...
http://www.diva-portal.org/smash/record.jsf?pid=diva2:525328
Free Monte Carlo Simulations Download
Free Monte Carlo Simulations Download,Monte Carlo Simulations is MCS is a tool that exploits the Monte Carlo method and with a complex algorithm.
http://www.brothersoft.com/monte-carlo-simulations-download-303277.html
Alexander Shkolnik, CRMR Postdoctoral Scholar, to give contributed session at the International Conference on Monte Carlo...
Alexander Shkolnik, CRMR Postdoctoral Scholar, to give contributed session at the MCM 2017 International Conference on Monte Carlo Methods and Applications. Session title: Compactness Approaches for Importance Sampling. From the conference website: Nine invited plenary speakers will give one-hour talks, with discussion. All other talks will last 30 minutes including questions and discussion. They will be split into sessions of 3 or 4 talks. Some special sessions devoted to a particular topic will be organized by designated or volunteer chairpersons. To promote true exchanges of ideas, it is preferred that all speakers in any given special session have different affiliations, and we will not accept a special session in which more than two speakers have the same affiliation. The other sessions will contain contributed talks accepted on the basis of submitted abstracts.. See the program and event details ». ...
http://cdar.berkeley.edu/event/alexander-shkolnik-crmr-postdoctoral-scholar-to-give-contributed-session-at-the-international-conference-on-monte-carlo-methods-and-applications/
History-by-History Variance in Monte Carlo Simulation of Radiation Interactions with MatterHistory-by-History Variance in Monte Carlo Simulation of Radiation Interactions with Matter
Radiation interaction with matter is by nature stochastic. Monte Carlo simulation makes possible, practical, economical and ethical, many experiments otherwise not. In-silico simulation of random samples of radiation histories places into our hands data which may be treated and analysed in radically different ways. This work reports history-by-history computation of the variance in simulation output from FLUKA, a general-purpose Monte Carlo code. Variance computed history by history is compared with variance estimated from varying batch sizes. This work also addresses the issue of under-sampling where, against conventional expectations, the variance spikes as we sample more histories. The background to the spikes is traced by reconstructing single histories interaction by interaction-a novel level of details atypical of Monte Carlo simulations in the field, where statistical convergence of averaged quantities has been the focus.
http://www.scirp.org/Journal/PaperInformation.aspx?paperID=74808&
Implementation and validation of an add-on MLC for Monte Carlo dose calculations in conformal stereotactic radiosurgeryImplementation and validation of an add-on MLC for Monte Carlo dose calculations in conformal stereotactic radiosurgery
inproceedings{391142, author = {Crop, Frederik and Reynaert, Nick and De Gersem, Werner and Pittomvils, Geert and COGHE, MARC and De Neve, Wilfried and Vakaet, Luc and Thierens, Hubert}, booktitle = {RADIOTHERAPY AND ONCOLOGY}, issn = {0167-8140}, language = {eng}, pages = {S419-S419}, publisher = {ELSEVIER IRELAND LTD}, title = {Implementation and validation of an add-on MLC for Monte Carlo dose calculations in conformal stereotactic radiosurgery}, volume = {81}, year = {2006 ...
https://biblio.ugent.be/publication/391142
A Monte Carlo Analysis of Technical Inefficiency PredictorsA Monte Carlo Analysis of Technical Inefficiency Predictors
Downloadable! This paper studies performance of both point and interval predictors of technical inefficiency in the stochastic production frontier model using a Monte Carlo experiment. In point prediction we use the Jondrow et al. (1980) point predictor of technical inefficiency, while for interval prediction the Horrace and Schmidt (1996) and Hjalmarsson et al. (1996) results are used. When ML estimators are used we find negative bias in point predictions. MSEs are found to decline as the sample size increases. The mean empirical coverage accuracy of the confidence intervals are significantly below the theoretical confidence level for all values of the variance ratio.
https://ideas.repec.org/p/hhs/hastef/0229.html
Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method<...Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method<...
TY - JOUR. T1 - Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method. AU - Sadi, T.. AU - Kelsall, R.. AU - Pilgrim, N.. PY - 2006/8. Y1 - 2006/8. N2 - The electrothermal simulator developed in this work uses an iterative procedure that self-consistently couples a Monte Carlo electronic trajectory simulation with a fast Fourier series solution of the heat diffusion equation. Results presented in this paper are obtained from the simulation of In0.15Ga0.85As/Al0.28Ga0.72As HEMTs. The negative differential output conductance (thermal droop) is observed in the electrothermal Ids-Vds characteristics of the simulated devices. Temperature profiles across the simulated region corresponding to different heat generation distributions are shown to be nonuniform with peak temperature and temperature range values dependent upon the device bias. The microscopic details of charge transport are studied, and the relationship between the thermal droop and the ...
https://abdn.pure.elsevier.com/en/publications/simulation-of-electron-transport-in-ingaasalgaas-hemts-using-an-e
0811.4375] Off-Lattice Self-Learning Kinetic Monte Carlo: Application to 2D Cluster Diffusion on the fcc(111) Surface0811.4375] Off-Lattice Self-Learning Kinetic Monte Carlo: Application to 2D Cluster Diffusion on the fcc(111) Surface
Abstract: We report developments of the kinetic Monte Carlo (KMC) method with improved accuracy and increased versatility for the description of atomic diffusivity on metal surfaces. The on-lattice constraint built into our recently proposed Self-Learning KMC (SLKMC) [1] is released, leaving atoms free to occupy Off-Lattice positions to accommodate several processes responsible for small-cluster diffusion, periphery atom motion and hetero-epitaxial growth. The technique combines the ideas embedded in the SLKMC method with a new pattern recognition scheme fitted to an Off-Lattice model in which relative atomic positions is used to characterize and store configurations. Application of a combination of the drag and the Repulsive Bias Potential (RBP) methods for saddle points searches, allows the treatment of concerted cluster, and multiple and single atom motions on equal footing. This tandem approach has helped reveal several new atomic mechanisms which contribute to cluster migration. We present ...
http://export.arxiv.org/abs/0811.4375
Incorporation of multivariate statistical distribution of magnitude-distance and Monte-Carlo simulation in probabilistic...Incorporation of multivariate statistical distribution of magnitude-distance and Monte-Carlo simulation in probabilistic...
TY - JOUR. T1 - Incorporation of multivariate statistical distribution of magnitude-distance and Monte-Carlo simulation in probabilistic seismic hazard analysis. AU - Azarbakht, Alireza. AU - Ebrahimi, Mohammad Ali. PY - 2019/7/31. Y1 - 2019/7/31. N2 - The classical seismic hazard analysis is based on two independent simplified assumptions including the statistical distribution of magnitude (usually Gutenberg-Richter 1958) and the distance distribution (equal probability in each point of a given source). However, the interaction between the two distributions is rarely discussed in past researches. Therefore, a joint M-R distribution has been implemented in this paper in order to shed light into these simplified assumptions. The Tehran metropolis is considered as the case study since it locates in a highly active seismic region. Three seismological datasets were used in this study, i.e. the observed dataset, the simulated dataset based on the Han and Choi 2008 methodology, and the simulated ...
https://pureportal.strath.ac.uk/en/publications/incorporation-of-multivariate-statistical-distribution-of-magnitu
Scale property Monte Carlo driven inversion of surface wave data | EarthdocScale property Monte Carlo driven inversion of surface wave data | Earthdoc
Analysis of surface wave data can be made using probabilistic approaches, e.g. Monte Carlo methods that employ a random or pseudo-random generator. A method like this is required to efficiently avoid local minima, evaluate non-uniqueness in the solution and estimating the values and uncertainties of the model parameters. The pure Monte Carlo method applied to surface wave inversion becomes efficient with the introduction of a 'smart sampling' rule which exploits the scale property (scaling of the modal solution with the wavelength) of the solution. Introducing this property in the Monte Carlo inversion focuses the scan of model space on high probability density zones. Each model is scaled before evaluating the misfit in order to bring the theoretical dispersion curve obtained by forward algorithm closer to the experimental. An applicative example is presented to support our hypothesis. <br>The main advantage of the proposed approach, based on scale property of dispersion curves, is the
https://www.earthdoc.org/content/papers/10.3997/2214-4609.201402745
A Monte Carlo approach to describe the reduction profiles of bidimensional MoOx structures grown on an alumina sustrateA Monte Carlo approach to describe the reduction profiles of bidimensional MoOx structures grown on an alumina sustrate
4 Instituto de Catálisis y Petroleoquímica, CSIC, Cantoblanco, E-28049-Madrid, Spain Abstract The reducibility of molybdenum oxide supported on γ-Al2O3 was studied by temperature programmed reduction (TPR). Interpretation of the reduction profiles was successfully achieved by applying the Monte Carlo method and taking into account the presence of different molybdenum species on the support surface. Seven reactions were assumed to take place along the reduction process. The first hydrogen consumption peak was assigned to the reduction of Mo6+ ions present as well-dispersed polymeric species into Mo+5, while the second one accounts for the reduction not of the Mo5+ ions generated in the first reduction into Mo+4 but also for the reduction of monomeric Mo6+species. Keywords Mo Catalysts. Monte Carlo Method. Temperature Programmed Reduction. I. INTRODUCTION The interaction of molybdenum oxides on the alumina surface and their reduction processes have been extensively investigated as documented in ...
http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0327-07932007000400012&lng=es&nrm=iso&tlng=en
Advanced Analytic Techniques: Summary of Findings: Monte Carlo Simulations (3.5 out of 5 Stars)
The class was given a graph with the x-axis numbering up to 24. They were required to start at the number 20 and after rolling a six sided dice, subtract the number rolled from 20 and keep rolling until they got to zero. They plotted the numbers they rolled on a graph to see how many turns it took to get to zero. This was done five times for each student. The number of attempts was collected from everyone in class and average was calculated for the number of attempts required for a dice to reduce 20 to zero. The method used in class was a simplistic process of Monte Carlo, in a real life scenario the method is repeated 1,000 or more times to get a better output. Our exercise launched a discussion over the actual value of a monte carlo simulation compared to other forms of regression. We came to the conclusion that Monte Carlo does offer different kinds of answers that could be more valuable in the uncertain world of intelligence analysis. ...
http://advat.blogspot.co.uk/2017/11/summary-of-findings-monte-carlo.html
AES E-Library » Restoration of Nonlinearly Distorted Audio Using Markov Chain Monte Carlo MethodsAES E-Library » Restoration of Nonlinearly Distorted Audio Using Markov Chain Monte Carlo Methods
We present a method for restoration of audio which has been distorted by a nonlinear channel or recording medium. We model the signal as a linear process cascaded with a nonlinear distortion. We use sampling methods to perform model selection and parameter estimation. We allow the linear model to be nonstationary, and present results for both synthetic and real distortions.
http://www.aes.org/e-lib/browse.cfm?elib=8501
How many operating rooms are needed to manage non-elective surgical cases? A Monte Carlo simulation study | BMC Health Services...
The present study demonstrates a simulation approach to determine the resources needed to handle urgent surgical cases. We performed a sensitivity analysis and found how wait times change as the result of changing the number of ORs, the service time (e.g., how long resources are devoted to the patient) and surgical volume. The parameters of the program (which is freely available) can be adjusted according to the characteristics of individual hospitals. For example, the number of ORs needed to achieve acceptable wait times will depend on the arrival rate of patients, length of surgical procedures and preparation/clean-up time specific to each hospital.. In the present simulation model the arrival time equates to when the decision is made to perform surgery, and the wait time is the time between the arrival time and when the patient enters the OR. The interpretation of that wait time is made from a clinically relevant perspective, i.e., how long can the patient wait before a further delay would ...
http://bmchealthservres.biomedcentral.com.preview-live.oscarjournals.springer.com/articles/10.1186/s12913-015-1148-x
Reversible jump MCMC for volumetric calibration. - Lancaster EPrintsReversible jump MCMC for volumetric calibration. - Lancaster EPrints
Volumetric calibration is used for high accuracy estimation of the functional volume-to-level relationship for vessels used to store hazardous liquids. An example of calibration of a vessel for reprocessed nuclear material is used to illustrate an analysis of calibration data. We advocate a Bayesian approach, with proper account of genuine prior information, using a reversible jump Markov chain Monte Carlo method for estimation.. ...
http://eprints.lancs.ac.uk/19348/
Rao-Blackwellization of Particle Markov Chain Monte Carlo Methods Using Forward Filtering Backward Sampling - IEEE Journals &...Rao-Blackwellization of Particle Markov Chain Monte Carlo Methods Using Forward Filtering Backward Sampling - IEEE Journals &...
Smoothing in state-space models amounts to computing the conditional distribution of the latent state trajectory, given observations, or expectations of fu
http://ieeexplore.ieee.org/document/5940249/?reload=true&Citations&tp=&arnumber=5940249&contentType=Journals+%26+Magazines&sortType%3Dasc_p_Sequence%26filter%3DAND
Radyalis Releases Next-Generation Monte Carlo Proton Therapy Planning | Imaging Technology NewsRadyalis Releases Next-Generation Monte Carlo Proton Therapy Planning | Imaging Technology News
July 22, 2014 - Radyalis is showcasing its high-performance Monte Carlo dose calculation product for proton applications simultaneously at the 2014 annual meeting of the American Association of Physics in Medicine (AAPM) in Austin, Texas.. The companys work in large-scale optimization and high-performance computing enables clinicians to utilize the high-accuracy benefits of Monte Carlo at unprecedented speeds for proton therapy dose modeling. The Monte Carlo engine changes radiotherapy workflow as the most complex patient treatment plans can be evaluated for efficacy and biological safety with high confidence in a matter of minutes.. The technology combines new algorithmic and software architecture advances for very high parallel efficiency on customers existing hardware, including single and multiple node installations, clusters and cloud platforms, for both Linux and Windows environments. Radyalis software is highly portable and very flexible, and designed to be integrated via a customer API ...
https://www.itnonline.com/content/radyalis-releases-next-generation-monte-carlo-proton-therapy-planning
Perform Monte Carlo simulationPerform Monte Carlo simulation
A process might contain randomness, making it impossible to predict its outcome with certainty. Instead, you can simulate the process. To do this, you have to simulate the randomness, which is what Monte Carlo simulation does. The Excel AnalysisTool Pak Random Number Generation tool is perfect for creating Monte Carlo simulations. Joe shows you how to use this tool to create simulations that use the Discrete distribution. Youll then be able to simulate a variety of processes.
https://www.lynda.com/Tableau-tutorials/Perform-Monte-Carlo-simulation/455718/499329-4.html
Thermodynamics of L10 ordering in FePt nanoparticles studied by Monte Carlo simulations based on an analytic bond-order...Thermodynamics of L10 ordering in FePt nanoparticles studied by Monte Carlo simulations based on an analytic bond-order...
The size dependence of the order-disorder transition in FePt nanoparticles with an L10 structure is investigated by means of Monte Carlo simulations based on an analytic bond-order potential for FePt. A cross parametrization for the Fe-Pt interaction is proposed, which complements existing potentials for the constituents Fe and Pt. This FePt potential properly describes structural properties of ordered and disordered phases, surface energies, and the L10 to A1 transition temperature in bulk FePt. The potential is applied for examining the ordering behavior in small particles. The observed lowering of the order-disorder transition temperature with decreasing particle size confirms previous lattice-based Monte Carlo simulations [M. Müller and K. Albe, Phys. Rev. B 72, 094203 (2005)]. Although a distinctly higher amount of surface induced disorder is found in comparison to previous studies based on lattice-type Hamiltonians, the presence of lattice strain caused by the tetragonal distortion of the L10
https://materialsmodeling.org/publications/2007-Thermodynamics-of-L10-ordering-in-FePt-nanoparticles-studied-by-Monte-Carlo-simulations-based-on-an-analytic-bond-order-potential/
An Explanation of Signal Changes in DW-fMRI: Monte Carlo Simulation Study of Restricted Diffusion of Water Molecules Using 3D...
Japans largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies
https://www.jstage.jst.go.jp/article/transinf/E96.D/6/E96.D_1387/_article
Random vibration Monte Carlo simulation using multiple harmonic function schemes on Environmental XPRTRandom vibration Monte Carlo simulation using multiple harmonic function schemes on Environmental XPRT
Article Random vibration Monte Carlo simulation using multiple harmonic function schemes. The reliability study of structures under random vibration involves the performance of Monte Carlo simulation in time domain. One important part of such analyse...
https://www.environmental-expert.com/articles/random-vibration-monte-carlo-simulation-using-multiple-harmonic-function-schemes-462474
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction by Gerhard Winkler, Paperback |...Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction by Gerhard Winkler, Paperback |...
The Paperback of the Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction by Gerhard Winkler at Barnes & Noble.
https://www.barnesandnoble.com/w/image-analysis-random-fields-and-dynamic-monte-carlo-methods-gerhard-winkler/1112060636?ean=9783642975240
Simulating a diffusion on a graph. Application to reservoir engineering : Monte Carlo Methods and Applications mcmaSimulating a diffusion on a graph. Application to reservoir engineering : Monte Carlo Methods and Applications mcma
We develop a simple Monte Carlo method to compute the position at a given time of a diffusion on a graph, with constant speed on each edge. This method is exact, and we claim it could be used for simulating the position of a particle in a fissured media. Besides, we advocate that the notion of diffusions on graphs could be useful to understand the behavior of one-dimensional diffusions whose infinitesimal generator has piecewise constant coefficients. ...
https://www.degruyter.com/view/j/mcma.2003.9.issue-3/156939603322729003/156939603322729003.xml
Selective hydrogen production at Pt(111) investigated by Quantum Monte Carlo methods for metal catalysis - Tampere University...
TY - JOUR. T1 - Selective hydrogen production at Pt(111) investigated by Quantum Monte Carlo methods for metal catalysis. AU - Sharma, Rajesh O.. AU - Rantala, Tapio T.. AU - Hoggan, Philip E.. PY - 2020/6/5. Y1 - 2020/6/5. N2 - This rapid communication gives the salient points and results of the theoretical investigation of a chemical reaction for efficient selective hydrogen production. The clean fuel produced is a sustainable energy source. Accurate methods based on quantum theory are used because the changing electronic structure is a probe that monitors reactions. The reaction between water and carbon monoxide is used industrially with metal catalysts, usually platinum. There is a considerable economic and environmental challenge underpinning this fundamental investigation where bond dissociation plays an essential role. A bond dissociation process is often the limiting step of reaction rates for industrial catalysis. Most mainstream quantum approaches fail to a greater or lesser degree in ...
https://tutcris.tut.fi/portal/en/publications/selective-hydrogen-production-at-pt111-investigated-by-quantum-monte-carlo-methods-for-metal-catalysis
Introduction To Monte Carlo Methods - alexhwoodsIntroduction To Monte Carlo Methods - alexhwoods
Im going to keep this tutorial light on math, because the goal is just to give a general understanding. Monte Carlo methods originated from the Manhattan Project, as a way to simulate the distance neutrons would travel through through various materials [1]. Ideas using sampling had been around for a little while, but they took off…
https://alexhwoods.wordpress.com/2015/07/25/introduction-to-monte-carlo-methods/
Ebook Monte Carlo Methods In Quantum ProblemsEbook Monte Carlo Methods In Quantum Problems
ebook monte carlo methods from an electrical press is the security, downplays suggested in four superb products, and the sections have rooted by the four rays. From this fuel the study of science of commercialization s published. performing a third book from one quantum, or dissemination, to another is an composite Schedule in server -cultivation, which is the high teachers between the nice cases of classics( kW of emphasis) to truly understand and email question.
http://theintuitivedecision.com/wp-content/upgrade/ebook/ebook-monte-carlo-methods-in-quantum-problems/
Monte Carlo method - Find link
short range and of Coulomb 1/r form at long range. The diffusion Monte Carlo method has been used to obtain numerically exact results for the ...
http://edwardbetts.com/find_link/Monte_Carlo_method
Scratch Programming: The Area of Texas Using the Monte Carlo Method
In the previous post, the value of Pi was approximated by using the Monte Carlo Method. It might be easy to see that the circle-inscribed-in-a -square method used in the project can be generalized to shapes that are highly irregular, like the shape of Texas. ...
http://www.scratch-blog.com/2014/02/the-area-of-texas-using-monte-carlo.html
Monte Carlo methods for fissured porous media: a gridless approach - pdf descargar
Monte Carlo methods for fissured porous media: a gridless approach. . 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.
http://libros.duhnnae.com/2017/jun8/149835563058-Monte-Carlo-methods-for-fissured-porous-media-a-gridless-approach.php
Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression [PeerJ]Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression [PeerJ]
Background Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori. Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R) methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness) on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set). The original analysis of these data used a linear model (GLS) of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on
https://peerj.com/articles/2575/
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture ModelsParticle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
This paper presents an original Markov chain Monte Carlo method to sample from the posterior distribution of conjugate mixture models. This algorithm relies on a flexible split-merge procedure built using the particle Gibbs sampler introduced in Andrieu et al. (2009, 2010). The resulting so-called Particle Gibbs Split-Merge sampler does not require the computation of a complex acceptance ratio and can be implemented using existing sequential Monte Carlo libraries. We investigate its performance experimentally on synthetic problems as well as on geolocation data. Our results show that for a given computational budget, the Particle Gibbs Split-Merge sampler empirically outperforms existing split merge methods. The code and instructions allowing to reproduce the experiments is available at github.com/aroth85/pgsm.. PDF BibTeX ...
https://jmlr.org/beta/papers/v18/15-397.html
Study of the ST2 model of water close to the liquid-liquid critical point - Physical Chemistry Chemical Physics (RSC Publishing)Study of the ST2 model of water close to the liquid-liquid critical point - Physical Chemistry Chemical Physics (RSC Publishing)
We perform successive umbrella sampling grand canonical Monte Carlo computer simulations of the original ST2 model of water in the vicinity of the proposed liquid-liquid critical point, at temperatures above and below the critical temperature. Our results support the previous work of Y. Liu, A. Z. Panagiotop Physics and chemistry of ice and water
http://pubs.rsc.org/en/content/articlelanding/2011/cp/c1cp22316j
Advances in Quantum Monte Carlo - James B. Anderson; Stuart M. Rothstein - Oxford University PressAdvances in Quantum Monte Carlo - James B. Anderson; Stuart M. Rothstein - Oxford University Press
Advances in Quantum Monte Carlo confronts the challenges in quantum mechanics that have become progressively more prevalent in the last five years. This book will cover the needed advances in Quantum Monte Carlo methods including improvements and a complete range of applications. Advances in Quantum Monte Carlo will also include a complete spectrum of applications.
https://global.oup.com/academic/product/advances-in-quantum-monte-carlo-9780841274167?lang=en&cc=usvNumResPerPage=20vNumResPerPage=20vNumResPerPage=20vNumResPerPage=20
Monte Carlo methodMonte Carlo method
Mathematics portal Auxiliary field Monte Carlo Biology Monte Carlo method Direct simulation Monte Carlo Dynamic Monte Carlo ... for Monte Carlo molecular modeling Mean-field particle methods Monte Carlo method for photon transport Monte Carlo methods for ... electron transport Monte Carlo N-Particle Transport Code Morris method Multilevel Monte Carlo method Quasi-Monte Carlo method ... a Monte Carlo method, and a Monte Carlo simulation: a simulation is a fictitious representation of reality, a Monte Carlo ...
https://en.wikipedia.org/wiki/Monte_Carlo_method
Quasi-Monte Carlo methodQuasi-Monte Carlo method
Monte Carlo method Monte Carlo methods in finance Quasi-Monte Carlo methods in finance Biology Monte Carlo method Computational ... on Monte Carlo and quasi-Monte Carlo methods A very intuitive and comprehensive introduction to Quasi-Monte Carlo methods (Use ... Hence, the Monte Carlo method and the quasi-Monte Carlo method are beneficial in these situations. The approximation error of ... Hence, in general, the accuracy of the quasi-Monte Carlo method increases faster than that of the Monte Carlo method. However, ...
https://en.wikipedia.org/wiki/Quasi-Monte_Carlo_method
Dynamic Monte Carlo methodDynamic Monte Carlo method
In chemistry, dynamic Monte Carlo (DMC) is a Monte Carlo method for modeling the dynamic behaviors of molecules by comparing ... It is essentially the same as Kinetic Monte Carlo. Unlike the Metropolis Monte Carlo method, which has been employed to study ... Large-scale systems are able to be calculated by this method. Hybrid Monte Carlo (Meng and Weinberg 1994): B. Meng and W. H. ... v t e (Monte Carlo methods, Computational chemistry, All stub articles, Chemistry stubs, Computational science stubs). ...
https://en.wikipedia.org/wiki/Dynamic_Monte_Carlo_method
Multilevel Monte Carlo methodMultilevel Monte Carlo method
... and the combination of MLMC with the Quasi-Monte Carlo method. Monte Carlo method Monte Carlo methods in finance Quasi-Monte ... is then approximated by a Monte Carlo method, resulting in the multilevel Monte Carlo method. Note that taking a sample of the ... end until converged Recent extensions of the multilevel Monte Carlo method include multi-index Monte Carlo, where more than one ... MLMC methods can greatly reduce the computational cost of standard Monte Carlo methods by taking most samples with a low ...
https://en.wikipedia.org/wiki/Multilevel_Monte_Carlo_method
Biology Monte Carlo methodBiology Monte Carlo method
Biology Monte Carlo methods (BioMOCA) have been developed at the University of Illinois at Urbana-Champaign to simulate ion ... namely the Boltzmann transport Monte Carlo (BTMC) and particle-particle-particle-mesh (P3M). The first one uses Monte Carlo ... Monte Carlo method Biology Computational biology van der Straaten, T.A.; Kathawala, G.; Trellakis, A.; Eisenberg §, R.S.; ... C. Jacoboni, P. Lugli, The Monte Carlo Method for Semiconductor Device Simulation, Springer Verlag, New York (1989) R. Hockney ...
https://en.wikipedia.org/wiki/Biology_Monte_Carlo_method
Monte Carlo methods in financeMonte Carlo methods in finance
Monte Carlo methods are ideally suited to evaluating difficult integrals (see also Monte Carlo method). Thus if we suppose that ... Quasi-Monte Carlo methods in finance Monte Carlo method Historical simulation (finance) Stock market simulator Real options ... Monte Carlo:methodologies and applications for pricing and risk management. Risk. Paul Glasserman (2003). Monte Carlo methods ... General Monte Carlo Simulation (Encyclopedia of Quantitative Finance), Peter Jaeckel and Eckhard Plateny Monte Carlo Method, ...
https://en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance
Monte Carlo method in statistical physicsMonte Carlo method in statistical physics
Monte Carlo integration Metropolis algorithm Importance sampling Quantum Monte Carlo Monte Carlo molecular modeling Allen, M.P ... Monte Carlo in statistical physics refers to the application of the Monte Carlo method to problems in statistical physics, or ... For those systems, the Monte Carlo integration (and not to be confused with Monte Carlo method, which is used to simulate ... The general motivation to use the Monte Carlo method in statistical physics is to evaluate a multivariable integral. The ...
https://en.wikipedia.org/wiki/Monte_Carlo_method_in_statistical_physics
Monte Carlo methods for option pricingMonte Carlo methods for option pricing
Monte Carlo methods in finance Quasi-Monte Carlo methods in finance Stochastic modelling (insurance) Stochastic asset model ... In mathematical finance, a Monte Carlo option model uses Monte Carlo methods to calculate the value of an option with multiple ... Monte Carlo methods in finance. John Wiley and Sons. ISBN 978-0-471-49741-7. Don L. McLeish (2005). Monte Carlo Simulation & ... Monte Carlo Statistical Methods. ISBN 978-0-387-21239-5. Online tools Monte Carlo simulated stock price time series and random ...
https://en.wikipedia.org/wiki/Monte_Carlo_methods_for_option_pricing
Quasi-Monte Carlo methods in financeQuasi-Monte Carlo methods in finance
The quasi-Monte Carlo (QMC) method is defined by φ Q M C ( f ) = 1 n ∑ i = 1 n f ( x i ) , {\displaystyle \varphi ^{\mathop {\ ... To break this curse of dimensionality one can use the Monte Carlo (MC) method defined by φ M C ( f ) = 1 n ∑ i = 1 n f ( x i ... The standard terminology quasi-Monte Carlo is somewhat unfortunate since MC is a randomized method whereas QMC is purely ... It is well known that the expected error of Monte Carlo is of order n − 1 / 2 {\displaystyle n^{-1/2}} . Thus, the cost of the ...
https://en.wikipedia.org/wiki/Quasi-Monte_Carlo_methods_in_finance
Monte Carlo method for photon transportMonte Carlo method for photon transport
This flexibility makes Monte Carlo modeling a powerful tool. Thus, while computationally inefficient, Monte Carlo methods are ... in biological tissue Monte Carlo method Convolution for optical broad-beam responses in scattering media Monte Carlo methods ... Sweden GPU acceleration of Monte Carlo simulations and scalable Monte Carlo. Open source code for download. Cloud-based Monte ... Due to the nature of PDT, it is useful to use Monte Carlo methods for modeling scattering and absorption in the tissue in order ...
https://en.wikipedia.org/wiki/Monte_Carlo_method_for_photon_transport
Monte Carlo methods for electron transportMonte Carlo methods for electron transport
Monte Carlo method Semiconductor device Monte Carlo method for photon transport Band structure Method of quantum ... characteristics Quantum Monte Carlo Quasi-Monte Carlo method Karl Hess, ed. (1991). Monte Carlo Device Simulation: Full Band ... The Monte Carlo method for electron transport is a semiclassical Monte Carlo (MC) approach of modeling semiconductor transport ... whereas the Monte Carlo is the stochastic approach used to solve the BTE. The semiclassical Monte Carlo method is a statistical ...
https://en.wikipedia.org/wiki/Monte_Carlo_methods_for_electron_transport
Monte Carlo tree searchMonte Carlo tree search
Rémi Coulom described the application of the Monte Carlo method to game-tree search and coined the name Monte Carlo tree search ... the basic version of Monte Carlo tree search converges only in so called "Monte Carlo Perfect" games. However, Monte Carlo tree ... Monte Carlo) trees and was the main seed for UCT.) Although it has been proven that the evaluation of moves in Monte Carlo tree ... There are automated methods to tune the parameters to maximize the win rate. Monte Carlo tree search can be concurrently ...
https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
Path integral Monte CarloPath integral Monte Carlo
... (PIMC) is a quantum Monte Carlo method used to solve quantum statistical mechanics problems ... As with all Monte Carlo method based approaches, a large number of points must be calculated. In principle, as more path ... The application of Monte Carlo methods to path integral simulations of condensed matter systems was first pursued in a key ... Freeman, David L; Doll, J. D (1994). "Fourier path integral Monte Carlo method for the calculation of the microcanonical ...
https://en.wikipedia.org/wiki/Path_integral_Monte_Carlo
Hamiltonian Monte CarloHamiltonian Monte Carlo
... from scratch Optimization and Monte Carlo Methods (Monte Carlo methods, Markov chain Monte Carlo). ... The Hamiltonian Monte Carlo algorithm (originally known as hybrid Monte Carlo) is a Markov chain Monte Carlo method for ... Dynamic Monte Carlo method Software for Monte Carlo molecular modeling Stan Metropolis-adjusted Langevin algorithm Duane, Simon ... Barbu, Adrian; Zhu, Song-Chun (2020). "Hamiltonian and Langevin Monte Carlo". Monte Carlo Methods. Singapore: Springer. pp. 281 ...
https://en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo
Markov chain Monte CarloMarkov chain Monte Carlo
Random walk Monte Carlo methods are a kind of random simulation or Monte Carlo method. However, whereas the random samples of ... Atzberger, P. "An Introduction to Monte-Carlo Methods" (PDF). Berg, Bernd A. (2004). Markov Chain Monte Carlo Simulations and ... Simulation and the Monte Carlo Method (2nd ed.). Wiley. ISBN 978-0-470-17794-5. Smith, R.L. (1984). "Efficient Monte Carlo ... Markov chain Monte Carlo, Monte Carlo methods, Computational statistics, Markov models, Bayesian estimation). ...
https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo
Continuous-time quantum Monte CarloContinuous-time quantum Monte Carlo
To distinguish it from other Monte Carlo methods for such systems that also work in continuous time, the method is then usually ... Rubtsov, A.N.; Savkin, V.V.; Lichtenstein, A.I. (2005). "Continuous-time quantum Monte Carlo method for fermions". Phys. Rev. B ... The attribute continuous-time was introduced to distinguish the method from the then-predominant Hirsch-Fye quantum Monte Carlo ... Assaad, F.F.; Lang, T.C. (2007). "Diagrammatic determinantal quantum Monte Carlo methods: Projective schemes and applications ...
https://en.wikipedia.org/wiki/Continuous-time_quantum_Monte_Carlo
Monte Carlo algorithmMonte Carlo algorithm
Monte Carlo methods, algorithms used in physical simulation and computational statistics based on taking random samples ... The term "Monte Carlo" was first introduced in 1947 by Nicholas Metropolis. Las Vegas algorithms are a dual of Monte Carlo ... Metropolis, N. (1987). "The beginning of the Monte Carlo method" (PDF). Los Alamos Science (1987 Special Issue dedicated to ... A false-biased Monte Carlo algorithm is always correct when it returns false; a true-biased algorithm is always correct when it ...
https://en.wikipedia.org/wiki/Monte_Carlo_algorithm
Monte Carlo CasinoMonte Carlo Casino
... a quantum Monte Carlo method Casino, a portion of the Circuit de Monaco of the Monaco Grand Prix The Casino de Monte-Carlo main ... It includes a casino, the Opéra de Monte-Carlo, and the office of Les Ballets de Monte-Carlo. The Casino de Monte-Carlo is ... The Monte Carlo Casino, officially named Casino de Monte-Carlo, is a gambling and entertainment complex located in Monaco. ... The Monte Carlo method, a computational approach which relies on repeated random sampling to solve difficult numerical problems ...
https://en.wikipedia.org/wiki/Monte_Carlo_Casino
Reptation Monte CarloReptation Monte Carlo
... is a quantum Monte Carlo method. It is similar to Diffusion Monte Carlo, except that it works with paths ... In both diffusion Monte Carlo and reptation Monte Carlo, the method first aims to solve the time-dependent Schrödinger equation ... This is how diffusion Monte Carlo works. Reptation Monte Carlo works in a very similar way, but is focused on the paths that ... S. Baroni & S. Moroni (1999). "Reptation Quantum Monte Carlo: A Method for Unbiased Ground-State Averages and Imaginary-Time ...
https://en.wikipedia.org/wiki/Reptation_Monte_Carlo
Variational Monte CarloVariational Monte Carlo
In computational physics, variational Monte Carlo (VMC) is a quantum Monte Carlo method that applies the variational method to ... 1984). Monte Carlo Methods in Quantum Problems. Dordrecht: Springer Netherlands. doi:10.1007/978-94-009-6384-9. ISBN 978-94-009 ... Metropolis-Hastings algorithm Rayleigh-Ritz method Time-dependent variational Monte Carlo McMillan, W. L. (19 April 1965). " ... Following the Monte Carlo method for evaluating integrals, we can interpret , Ψ ( X , a ) , 2 ∫ , Ψ ( X , a ) , 2 d X {\ ...
https://en.wikipedia.org/wiki/Variational_Monte_Carlo
Monte Carlo N-Particle Transport CodeMonte Carlo N-Particle Transport Code
Safety code (nuclear reactor) Nuclear data Monte Carlo method Monte Carlo methods for electron transport Nuclear reactor ... The Monte Carlo method for radiation particle transport has its origins at LANL dates back to 1946. The creators of these ... Monte Carlo software, Physics software, Fortran software, Scientific simulation software, Monte Carlo particle physics software ... Monte Carlo methods for particle transport have been driving computational developments since the beginning of modern computers ...
https://en.wikipedia.org/wiki/Monte_Carlo_N-Particle_Transport_Code
Gaussian quantum Monte CarloGaussian quantum Monte Carlo
... is a quantum Monte Carlo method that shows a potential solution to the fermion sign problem ... Corney, J. F.; Drummond, P. D. (2004-12-20). "Gaussian Quantum Monte Carlo Methods for Fermions and Bosons". Physical Review ... "Symmetry projection schemes for Gaussian Monte Carlo methods". Physical Review B. 72 (22): 224518. arXiv:cond-mat/0509149. ... Quantum Monte Carlo, All stub articles, Quantum chemistry stubs). ...
https://en.wikipedia.org/wiki/Gaussian_quantum_Monte_Carlo
Quantum Monte CarloQuantum Monte Carlo
The diverse flavors of quantum Monte Carlo approaches all share the common use of the Monte Carlo method to handle the multi- ... Monte Carlo Determinant quantum Monte Carlo or Hirsch-Fye quantum Monte Carlo Hybrid quantum Monte Carlo Path integral Monte ... World-line quantum Monte Carlo Time-dependent variational Monte Carlo: An extension of the variational Monte Carlo to study the ... Reptation Monte Carlo: Recent zero-temperature method related to path integral Monte Carlo, with applications similar to ...
https://en.wikipedia.org/wiki/Quantum_Monte_Carlo
Diffusion Monte CarloDiffusion Monte Carlo
... (DMC) or diffusion quantum Monte Carlo is a quantum Monte Carlo method that uses a Green's function to ... P.J. Reynolds (1994). Monte Carlo Methods in Ab Initio Quantum Chemistry. World Scientific Lecture and Course Notes in ... Grimm, R.C; Storer, R.G (1971). "Monte-Carlo solution of Schrödinger's equation". Journal of Computational Physics. 7 (1): 134- ... We can do these integrals by Monte Carlo integration. Reynolds, Peter J.; Tobochnik, Jan; Gould, Harvey (1990). "Diffusion ...
https://en.wikipedia.org/wiki/Diffusion_Monte_Carlo
Direct simulation Monte CarloDirect simulation Monte Carlo
... (DSMC) method uses probabilistic Monte Carlo simulation to solve the Boltzmann equation for ... Direct Simulation Monte Carlo Method: Visual Simulation Programs created by GA Bird. DSMC Demo Applet by Greg Khanlarov Course ... Currently, the DSMC method has been applied to the solution of flows ranging from estimation of the Space Shuttle re-entry ... The DSMC method has been extended to model continuum flows (Kn < 1) and the results can be compared with Navier Stokes ...
https://en.wikipedia.org/wiki/Direct_simulation_Monte_Carlo
Monte Carlo integrationMonte Carlo integration
... is a technique for numerical integration using random numbers. It is a particular Monte Carlo method ... This method is particularly useful for higher-dimensional integrals. There are different methods to perform a Monte Carlo ... sequential Monte Carlo (also known as a particle filter), and mean-field particle methods. In numerical integration, methods ... which is the promised advantage of Monte Carlo integration against most deterministic methods that depend exponentially on the ...
https://en.wikipedia.org/wiki/Monte_Carlo_integration
Energy-based generative neural networkEnergy-based generative neural network
Barbu, Adrian; Zhu, Song-Chun (2020). Monte Carlo Methods. Springer. Wu, Ying Nian; Xie, Jianwen; Lu, Yang; Zhu, Song-Chun ( ... The parameter of the neural network in this model is trained in a generative manner by Markov chain Monte Carlo(MCMC)-based ... the algorithm samples the synthesized examples from the current model by a gradient-based MCMC method, e.g., Langevin dynamics ...
https://en.wikipedia.org/wiki/Energy-based_generative_neural_network
Low-discrepancy sequenceLow-discrepancy sequence
Discrepancy theory Markov chain Monte Carlo Quasi-Monte Carlo method Sparse grid Systematic sampling Beck, József (1989). "A ... this is the Monte Carlo method. If the points are chosen as elements of a low-discrepancy sequence, this is the quasi-Monte ... Elements need not be recomputed in the random Monte Carlo method if N is increased, but the point sets do not have minimal ... Monte Carlo Methods. doi:10.1007/978-94-009-5819-7. ISBN 978-94-009-5821-0. Herman Tulleken. Tulleken, Herman (March 2008). " ...
https://en.wikipedia.org/wiki/Low-discrepancy_sequence
Non-uniform random variate generationNon-uniform random variate generation
The first methods were developed for Monte-Carlo simulations in the Manhattan project,[citation needed] published by John von ... Monte Carlo Methods. National Bureau of Standards Applied Mathematics Series. Vol. 12. US Government Printing Office. pp. 36-38 ... New York: Springer Fishman, G.S. (1996) Monte Carlo. Concepts, Algorithms, and Applications. New York: Springer Hörmann, W.; J ... Alias method, computational time is constant, using some pre-computed tables. There are other methods that cost constant time. ...
https://en.wikipedia.org/wiki/Non-uniform_random_variate_generation
Reverse Monte CarloReverse Monte Carlo
The Reverse Monte Carlo (RMC) modelling method is a variation of the standard Metropolis-Hastings algorithm to solve an inverse ... There are four publicly available implementations of the RMC method. Fundamental Library Language for Reverse Monte Carlo or ... Articles with short description, Short description matches Wikidata, Use dmy dates from September 2015, Monte Carlo methods, ... Hybrid Reverse Monte Carlo method with silicon, carbon, germanium and silicon carbide potentials". Computer Physics ...
https://en.wikipedia.org/wiki/Reverse_Monte_Carlo
Uncertainty quantificationUncertainty quantification
Techniques such as the Monte Carlo method are frequently used. A probability distribution can be represented by its moments (in ... Local expansion-based methods: Taylor series, perturbation method, etc. These methods have advantages when dealing with ... Monte Carlo simulations, importance sampling, adaptive sampling, etc. General surrogate-based methods: In a non-instrusive ... Most probable point (MPP)-based methods: first-order reliability method (FORM) and second-order reliability method (SORM). ...
https://en.wikipedia.org/wiki/Uncertainty_quantification
Muon tomographyMuon tomography
Tanaka, H.K.M. (2007). "Monte-Carlo simulations of atmospheric muon production: Implication of the past martian environment". ... New methods of warhead imaging are of crucial importance for the success of mutual inspections. Muon Tomography can be used for ... The method used for this experiment could be applied to other archeological targets to check the structural integrity of ... reported using tomographic methods to track the passage of cosmic rays muons through cesium iodide crystals for quality control ...
https://en.wikipedia.org/wiki/Muon_tomography
Classical fluidClassical fluid
... can be studied using quantum Monte Carlo methods, Feynman path integral equation methods, and approximately via CHNC integral- ... equation methods. Bose-Einstein condensate Fermi liquid Many-body theory Quantum fluid R. Balescu, Equilibrium and ... An essentially very accurate way of determining the properties of classical fluids is provided by the method of molecular ...
https://en.wikipedia.org/wiki/Classical_fluid
Markov decision processMarkov decision process
For example, the dynamic programming algorithms described in the next section require an explicit model, and Monte Carlo tree ... Solutions for MDPs with finite state and action spaces may be found through a variety of methods such as dynamic programming. ... iterative method) This variant has the advantage that there is a definite stopping condition: when the array π {\displaystyle \ ... Lloyd Shapley's 1953 paper on stochastic games included as a special case the value iteration method for MDPs, but this was ...
https://en.wikipedia.org/wiki/Markov_decision_process
List of female dancersList of female dancers
Ballet Russe de Monte Carlo, Grand Ballet du Marquis de Cuevas, also performed on Broadway Cléo de Mérode (1875-1966), dancer, ... taught Isadora Duncan's method Lucie Kadlčáková (born 1982), ice dancer Daria Klimentová (born 1971), ballet dancer, former ... Ballet Russe de Monte Carlo, co-founder of University of Oklahoma School of Dance Lia Cirio, dancer, principal dancer, Boston ... Ballets de Monte-Carlo Caroline Truong (born 1980), ice dancer Marie van Goethem (born 1865, death unknown), dancer, model ...
https://en.wikipedia.org/wiki/List_of_female_dancers
Photon mappingPhoton mapping
A Monte Carlo method called Russian roulette is used to choose one of these actions. If the photon is absorbed, no new ... However, it is a consistent method, and the accuracy of a render can be increased by increasing the number of photons. As the ... This was the original way Jensen implemented it; however, the method becomes slow for highly scattering materials, and ... which means that averaging infinitely many renders of the same scene using this method does not converge to a correct solution ...
https://en.wikipedia.org/wiki/Photon_mapping
Stanislaw UlamStanislaw Ulam
"The Monte Carlo method". Metropolis and Ulam published the first unclassified paper on the Monte Carlo method in 1949. Fermi, ... The Monte Carlo method has become a ubiquitous and standard approach to computation, and the method has been applied to a vast ... Ulam, who was to become an originator of the Monte Carlo method and co-discoverer of the hydrogen-bomb,... used to tease me by ... The Day After Trinity at IMDb Eckhardt, Roger (1987). "Stan Ulam, John von Neumann, and the Monte Carlo Method" (PDF). Los ...
https://en.wikipedia.org/wiki/Stanislaw_Ulam
BackgammonBackgammon
In this method, a Monte-Carlo evaluation of positions is conducted (typically thousands of trials) where different random dice ... The Monte Carlo tournament draws hundreds of players and spectators, and is played over the course of a week. By the 21st ... Monte Carlo was universally acknowledged as the site of the World Backgammon Championship and has remained as such for thirty ... 50, section 1, part 2. Maxa, Rudy (September 6, 1981). "Where the Rich And the Royal Play Their Games-Monte Carlo's Seven-Day ...
https://en.wikipedia.org/wiki/Backgammon
Solid hydrogenSolid hydrogen
Quantum Monte Carlo methods together with a first-principles treatment of anharmonic vibrational effects have then been used to ... N. D. Drummond, B. Monserrat, J. H. Lloyd-Williams, P. Lopez Rios, C. J. Pickard and R. J. Needs (2015). "Quantum Monte Carlo ...
https://en.wikipedia.org/wiki/Solid_hydrogen
Comparison of Gaussian process softwareComparison of Gaussian process software
... gradient descent or Markov chain Monte Carlo. These columns are about the possibility of fitting datapoints simultaneously to a ... Ambikasaran, S.; Foreman-Mackey, D.; Greengard, L.; Hogg, D. W.; O'Neil, M. (1 Feb 2016). "Fast Direct Methods for Gaussian ...
https://en.wikipedia.org/wiki/Comparison_of_Gaussian_process_software
Pigeon-shootingPigeon-shooting
The popularity of pigeon shooting at Monte Carlo included creation of new types of gun stocks; the "Monte Carlo" comb. Pigeon ... efforts carried out in cities where feral pigeons are considered a nuisance and are often controlled using lethal methods. The ... "Monaco: an extraordinary history of unusual sports". Monte Carlo Legend. Archived from the original on 2020-02-21. Retrieved ... Match of Nations World Championship Monte Carlo, Monaco - Prix de Larvotto and Prix Gaston Rambaud Havana, Cuba - The Pan ...
https://en.wikipedia.org/wiki/Pigeon-shooting
Joseph ConradJoseph Conrad
He achieves nothing there and, wishing to improve his finances, tries his luck in Monte Carlo and loses the 800 fr[ancs] he had ... reference points for Conrad's fragmentary method [of presenting information about characters and events] include Picasso and T. ...
https://en.wikipedia.org/wiki/Joseph_Conrad
John TexterJohn Texter
... initiating Monte Carlo analyses and modeling of DNA photochemical processes, and a postdoctoral year with Eugene S. Stevens at ... as practical method of characterizing electrokinetics in concentrated dispersions by developing calibration methods to ... developing a time-dependent Hartree-Fock model for circular dichroism in saccharides and a Monte Carlo-based nonlinear ...
https://en.wikipedia.org/wiki/John_Texter
Electrostatic analyzerElectrostatic analyzer
... which includes the capability of performing Monte Carlo simulations on known test particles, thus providing the designer a ... Nuclear Instruments and Methods in Physics Research Section A. 500 (1-3): 62-67. Bibcode:2003NIMPA.500...62I. doi:10.1016/S0168 ...
https://en.wikipedia.org/wiki/Electrostatic_analyzer
Causal setsCausal sets
Monte-Carlo simulations have provided evidence for a continuum phase in 2D using the Benincasa-Dowker action. Causal dynamical ... Tests in conformally-flat spacetimes have shown these two methods to be accurate. An ongoing task is to develop the correct ...
https://en.wikipedia.org/wiki/Causal_sets
Anita LoosAnita Loos
... she had seen Audrey Hepburn in a hotel lobby in Monte Carlo. Gigi opened in the fall of 1951 and would run until the spring of ... She and the ear, nose and throat specialist who was treating her came up with a method of fixing Emerson's hypochondria. The ... Loos devised a method of cobbling together published reports of Manhattan social life and mailing them to a friend in New York ...
https://en.wikipedia.org/wiki/Anita_Loos
Nuclear powerNuclear power
Review of recent trends and analysis of future investments using Monte Carlo Simulation". Renewable and Sustainable Energy ... Commonly suggested methods to isolate LLFP waste from the biosphere include separation and transmutation, synroc treatments, or ... Nuclear power is one of the leading low carbon power generation methods of producing electricity, and in terms of total life- ... W Wayt Gibbs (2013-12-30). "Triple-threat method sparks hope for fusion". Nature. 505 (7481): 9-10. Bibcode:2014Natur.505....9G ...
https://en.wikipedia.org/wiki/Nuclear_power
Emily A. CarterEmily A. Carter
She pioneered the combination of ab initio quantum chemistry with kinetic Monte Carlo simulations (KMC), molecular dynamics (MD ... She has also proposed methods for protecting materials from failure. Carter has developed fast methods for orbital-free density ... Early contributions included methods for accurate description of molecules at the quantum level and an algorithm for ... She has developed ab initio quantum chemistry methods and applied them to the study of materials. ...
https://en.wikipedia.org/wiki/Emily_A._Carter
Pressure-temperature-time pathPressure-temperature-time path
... a Monte Carlo assessment and implications for tectonic interpretations". Geoscience Frontiers. 7 (4): 591-607. doi:10.1016/j. ... The Gibbs method formalism is a method used to analyze pressures and temperatures of zoned minerals and textural changes in ... Both methods have their advantages and limitations. The XRF method provides a non-biased estimation, but may neglect the ... the method of inversely inferring the metamorphic events from rock samples via traditional petrological investigation methods ( ...
https://en.wikipedia.org/wiki/Pressure-temperature-time_path
List of mathematics-based methodsList of mathematics-based methods
Sainte-Laguë method (voting systems) Schulze method (voting systems) Sequential Monte Carlo method Simplex method Spectral ... Method of moving frames (differential geometry) Method of successive substitution (number theory) Monte Carlo method ( ... Adams' method (differential equations) Akra-Bazzi method (asymptotic analysis) Bisection method (root finding) Brent's method ( ... Finite volume method (numerical analysis) Highest averages method (voting systems) Method of exhaustion Method of infinite ...
https://en.wikipedia.org/wiki/List_of_mathematics-based_methods
Power system simulationPower system simulation
Another technique to model variability is the Monte Carlo method, in which different combinations of inputs and resulting ... This method can be applied to simulations for system security and unit commitment risk, and it is increasingly being used to ... Other power flow solution methods like stochastic optimization incorporate the uncertainty found in modeling power systems by ...
https://en.wikipedia.org/wiki/Power_system_simulation
Autonomous research robotAutonomous research robot
These platforms can map buildings and navigate out-of-the-box, using SLAM and a variation on Monte Carlo method/Markov ... This method creates a human readable map of the robot's workspace that can control and track robots as they move. Evolution ...
https://en.wikipedia.org/wiki/Autonomous_research_robot
Talladega Nights: The Ballad of Ricky BobbyTalladega Nights: The Ballad of Ricky Bobby
He drives the #47 Old Spice Chevrolet Monte Carlo. Reilly previously had a role in Days of Thunder. Austin Grimm as 10-year-old ... When his life hits rock bottom, Bobby's estranged father Reese returns to remind him how to drive, using unorthodox methods ... He originally drove the #26 Laughing Clown Malt Liquor Chevrolet Monte Carlo until it became sponsored by Wonder Bread and ... While challenging Bobby, Girard drives the #55 Perrier Chevrolet Monte Carlo. Michael Clarke Duncan as Lucius Washington, ...
https://en.wikipedia.org/wiki/Talladega_Nights:_The_Ballad_of_Ricky_Bobby
Auguste EscoffierAuguste Escoffier
In 1884, Escoffier and his wife moved to Monte Carlo, where Escoffier was employed by César Ritz, manager of the new Grand ... restaurateur and culinary writer who popularized and updated traditional French cooking methods. Much of Escoffier's technique ...
https://en.wikipedia.org/wiki/Auguste_Escoffier
Éric MoulinesÉric Moulines
... coupling estimation and simulation problems with Monte Carlo Markov Chain Methods (MCMC). He has also developed numerous ... Sequential Monte Carlo smoothing for general state space hidden Markov models », The Annals of Applied Probability, 2011, pp. ... He is involved in the development of new waveform synthesis methods called PSOLA (pitch synchronous overlap and add). After ... He then devoted himself mainly to the application of Bayesian methods with applications in signal processing and statistics. ...
https://en.wikipedia.org/wiki/Éric_Moulines
Stratified samplingStratified sampling
In computational statistics, stratified sampling is a method of variance reduction when Monte Carlo methods are used to ... This method is generally used when a population is not a homogeneous group. Proportionate allocation uses a sampling fraction ... In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In ...
https://en.wikipedia.org/wiki/Stratified_sampling
Paul Wilson (nuclear engineer)Paul Wilson (nuclear engineer)
Engineering Toolkit Research contributed to new methods in widely used radiation transport software such as the Monte Carlo N- ... "Acceleration Techniques for Direct Use of CAD-Based Geometries in Monte Carlo Radiation Transport." International Conference on ... the next generation fuel cycle simulator Lead developer of the Direct Accelerated Geometry Monte Carlo Toolkit (DAGMC) CAD- ... "Adjoint-weighted tallies for k-eigenvalue calculations with continuous-energy Monte Carlo." Nucl. Sci. Eng. 168, no. 3 (2011): ...
https://en.wikipedia.org/wiki/Paul_Wilson_(nuclear_engineer)
Science and inventions of Leonardo da VinciScience and inventions of Leonardo da Vinci
Pedretti, Carlo (2006). Leonardo da Vinci. Surrey: Taj Books International. ISBN 978-1-8440-6036-8.Pedretti, Carlo (1982). ... How and why I do not describe my method of remaining under water, or how long I can stay without eating; and I do not publish ... There is a legend that Leonardo tested the flying machine on Monte Ceceri with one of his apprentices, and that the apprentice ... I also have types of mortars that are very convenient and easy to transport.... when a place cannot be reduced by the method of ...
https://en.wikipedia.org/wiki/Science_and_inventions_of_Leonardo_da_Vinci
Information-based complexityInformation-based complexity
In contrast the Monte Carlo method gives only a stochastic assurance. See Sloan and Woźniakowski When are Quasi-Monte Carlo ... People in finance have long been using the Monte Carlo method (MC), an instance of a randomized algorithm. Then in 1994 a ... discovered that the quasi-Monte Carlo (QMC) method using low discrepancy sequences beat MC by one to three orders of magnitude ... This research was published in the 1964 monograph Iterative Methods for the Solution of Equations. The general setting for ...
https://en.wikipedia.org/wiki/Information-based_complexity
GCMCGCMC
... a special operations group of the French Navy General Conference Mennonite Church Grand Canonical Monte Carlo methods, a ...
https://en.wikipedia.org/wiki/GCMC
Review: H. A. Meyer, Symposium on Monte Carlo methodsReview: H. A. Meyer, Symposium on Monte Carlo methods
Monte Carlo Methods - VideoLectures.NETMonte Carlo Methods - VideoLectures.NET
... importance sampling and standard Markov chain Monte Carlo (MCMC) methods. We will then discuss more advanced MCMC methods such ... importance sampling and standard Markov chain Monte Carlo (MCMC) methods. We will then discuss more advanced MCMC methods such ... as adaptive MCMC methods and auxiliary variable methods such as parallel tempering, particle MCMC methods and slice sampling. ... as adaptive MCMC methods and auxiliary variable methods such as parallel tempering, particle MCMC methods and slice sampling. ...
http://videolectures.net/mlss2011_doucet_montecarlo/
Introducing Monte Carlo Methods with R | R-bloggers - Part 3Introducing Monte Carlo Methods with R | R-bloggers - Part 3
Thats it!, "Introducing Monte Carlo Methods with R" is out, truly out, I have received a copy from Springer by express mail ... Introducing Monte Carlo Methods with R. Typos in Chapter 3. January 7, 2010 , xian ... Here are two more typos in the exercises of Chapter 3 of "Introducing Monte Carlo Methods with R". - due to the (later) ... "Introducing Monte Carlo Methods with R" is out!. December 9, 2009 , xian ...
https://www.r-bloggers.com/tag/introducing-monte-carlo-methods-with-r/page/3/
Randomized quasi-Monte Carlo methods with applications to quantitative risk managementRandomized quasi-Monte Carlo methods with applications to quantitative risk management
Erik Hintz (2022). Randomized quasi-Monte Carlo methods with applications to quantitative risk management. UWSpace. http://hdl. ... We use randomized quasi-Monte Carlo (RQMC) techniques to construct computational tools for working with normal mixture models, ... RQMC methods on triangles have recently been developed by K. Basu and A. Owen. We show that one of the proposed sequences has ... We explore how we can still use RQMC in this setting and propose several methods when sampling of W is only possible via a ...
https://uwspace.uwaterloo.ca/handle/10012/18218
Calculate Pi with Monte-Carlo-method - JSXGraph WikiCalculate Pi with Monte-Carlo-method - JSXGraph Wiki
Retrieved from "http://jsxgraph.org/wiki/index.php?title=Calculate_Pi_with_Monte-Carlo-method&oldid=2535" ...
http://jsxgraph.uni-bayreuth.de/wiki/index.php?title=Calculate_Pi_with_Monte-Carlo-method&oldid=2535
Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples | Založba PasadenaAdvanced Markov Chain Monte Carlo Methods: Learning from Past Samples | Založba Pasadena
Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Markov Chain Monte Carlo (MCMC) methods are now an ... Ocenite: Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Če ste že vpisani med kupce v spletni knjigarni ... Priporočite: Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. E-naslov, kamor želite poslati izbrano ... A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with ...
http://pasadena.si/knjiga/102603/
An introduction to Monte Carlo method for the Boltzmann equation | ESAIM: Proceedings and SurveysAn introduction to Monte Carlo method for the Boltzmann equation | ESAIM: Proceedings and Surveys
An introduction to Monte Carlo method for the Boltzmann equation. Lorenzo Pareschi and Giovanni Russo ... Probabilistic tools and Monte-Carlo approximations for some Boltzmann equations ESAIM: Proc., 2001, Vol. 10, p. 77-126. ... Numerical methods for Vlasov-Lorentz models ESAIM: Proc., 2001, Vol. 10, p. 201-210. ... A redistribution method for axisymmetric diffusion ESAIM: Proc., 1999, Vol. 7, p. 387-396. ...
https://www.esaim-proc.org/articles/proc/abs/2001/01/cemracs/cemracs.html
Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information<...Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information<...
In this paper, we explore the potential of Quasi Monte-Carlo (QMC) and Multilevel Monte-Carlo (MLMC) estimation to reduce ... In this paper, we explore the potential of Quasi Monte-Carlo (QMC) and Multilevel Monte-Carlo (MLMC) estimation to reduce ... In this paper, we explore the potential of Quasi Monte-Carlo (QMC) and Multilevel Monte-Carlo (MLMC) estimation to reduce ... In this paper, we explore the potential of Quasi Monte-Carlo (QMC) and Multilevel Monte-Carlo (MLMC) estimation to reduce ...
https://research-information.bris.ac.uk/en/publications/multilevel-and-quasi-monte-carlo-methods-for-the-calculation-of-t
An introduction of simulation utilizing the Monte Carlo method of analysis as a tool for U.S. Coast Guard personnel...An introduction of simulation utilizing the Monte Carlo method of analysis as a tool for U.S. Coast Guard personnel...
An introduction of simulation utilizing the Monte Carlo method of analysis as a tool for U.S. Coast Guard personnel ...
https://calhoun.nps.edu/handle/10945/11649
Improved Monte-Carlo method vs. hit-and-miss method | Statistics Help @ Talk Stats Forum
... the hit-and-miss method or the improved Monte-Carlo method?*... ... Improved Monte-Carlo method vs. hit-and-miss method. * Thread ... variance of hit-and-miss method),0. \(\sigma^2_M-\sigma^2_H,0\). Doesnt it imply improved Monte-Carlo method has a higher ... I am not understanding *Which is more accurate, the hit-and-miss method or the improved Monte-Carlo method?*. http://www-stat. ...
http://www.talkstats.com/threads/improved-monte-carlo-method-vs-hit-and-miss-method.49551/
Estimate Pi by Monte Carlo method - MATLAB Cody - MATLAB CentralEstimate Pi by Monte Carlo method - MATLAB Cody - MATLAB Central
Carlo_method\,,w:r,,w:t,https://en.wikipedia.org/wiki/Monte_Carlo_method,/w:t,,/w:r,,/w:hyperlink,,/w:p,,/w:body,,/w:document ... We can estimate the value of pi with random number by Monte Carlo method. ,/w:t,,/w:r,,/w:p,,w:p,,w:pPr,,w:pStyle w:val=\text ... We can estimate the value of pi with random number by Monte Carlo method. ,/span,,/span,,/div,,div style=block-size: 21px; ... w:t,,/w:r,,w:hyperlink w:docLocation=\https://en.wikipedia.org/wiki/Monte_ ...
https://se.mathworks.com/matlabcentral/cody/problems/47103
Assessment of a fully 3D Monte Carlo reconstruction method for preclinical PET with iodine-124 - Inserm - Institut national de...
The aim of this work was to assess the quantitative performance of a fully 3D Monte Carlo (MC) reconstruction for preclinical I ... Assessment of a fully 3D Monte Carlo reconstruction method for preclinical PET with iodine-124 M. Moreau 1, 2 I. Buvat 1, 2 L ... Assessment of a fully 3D Monte Carlo reconstruction method for preclinical PET with iodine-124. Physics in Medicine and Biology ... The aim of this work was to assess the quantitative performance of a fully 3D Monte Carlo (MC) reconstruction for preclinical I ...
https://www.hal.inserm.fr/inserm-01818704
Water | Free Full-Text | Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current...Water | Free Full-Text | Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current...
... the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. Several model ... Monte Carlo Method The Monte Carlo Method is a numerical statistical method that allows the replication of random behaviour of ... Rubinstein, R.Y.; Kroese, D.P. Simulation and the Monte Carlo Method; John Wiley & Sons: Hoboken, NJ, USA, 2016. [Google ... Assigning Probabilities and Application of Monte Carlo Method As explained in Section 2.6, we obtained 10,000 random observed ...
https://www.mdpi.com/2073-4441/10/8/1038
Probabilistic regressor chains with Monte Carlo methods
... Read J.;Martino L. 2020. Abstract. A large number and diversity of ... Monte Carlo methods. Multi-label classification. Multi-output regression. Particle filters. Regressor chains ... In particular we present Monte Carlo schemes in the framework of probabilistic chains. We show they can be effective, flexible ... In particular we present Monte Carlo schemes in the framework of probabilistic chains. We show they can be effective, flexible ...
https://www.iris.unict.it/handle/20.500.11769/537616
Application of the Monte Carlo Method for the Estimation of Uncertainty in Radiofrequency Field Spot MeasurementsApplication of the Monte Carlo Method for the Estimation of Uncertainty in Radiofrequency Field Spot Measurements
... ... 8] Azpurua, M., Tremola, C., Paez, E. (2011). Comparison of the GUM and Monte Carlo methods for the uncertainty estimation in ... Propagation of distributions using a Monte Carlo method, 1st Edition. JCGM 101:2008.. Search in Google Scholar ... Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties. Accreditation and ...
https://sciendo.com/es/article/10.1515/msr-2015-0011
Monte Carlo MethodsMonte Carlo Methods
A Markov Chain Monte Carlo Method for Derivative Pricing and Risk AssessmentA Markov Chain Monte Carlo Method for Derivative Pricing and Risk Assessment
... Sanjiv R. Das and Alistair Sinclair ... Monte Carlo simulation is used to estimate the lattice value by sampling appropriately from the nodes. Most sampling methods ... This paper presents a Markov chain Monte Carlo scheme, adapted from Sinclair and Jerrum (Information and Computation 82 (1989 ... This partial information is used to direct the sampling, in similar fashion to traditional importance sampling methods. The key ...
https://joim.com/articles-markov-chain-monte-carlo-method-derivative-pricing-risk-assessment/
Repositório Institucional da UnB: Monte Carlo methods on low and high energy atmospheric phenomenaRepositório Institucional da UnB: Monte Carlo methods on low and high energy atmospheric phenomena
Monte Carlo methods on low and high energy atmospheric phenomena. 2020. xviii, 143 f., il. Tese (Doutorado em Física)- ... Monte Carlo methods on low and high energy atmospheric phenomena. pt_BR. ... Através de simulações Monte Carlo, analiza-se os campos elétricos necessários para colocar um elétrons em estado de runaway. ... Monte Carlo simulations are implemented to investigate the probability of low electric fields driving electrons to runaway ...
https://repositorio.unb.br/handle/10482/39672?mode=full
Heuristic Monte Carlo method applied to cooperative motion algorithm for binary lattice fluid - IOS PressHeuristic Monte Carlo method applied to cooperative motion algorithm for binary lattice fluid - IOS Press
While the first method is the well-established Metr ... The Cooperative Motion Algorithm is an efficient lattice method ... Heuristic Monte Carlo method applied to cooperative motion algorithm for binary lattice fluid Article type: Research Article ... While the first method is the well-established Metropolis algorithm, the other one is an heuristic algorithm which needs ... We compare both methods, calculating the energy as well as heat capacity as a function of temperature. The critical temperature ...
https://content.iospress.com/articles/journal-of-computational-methods-in-sciences-and-engineering/jcm00508
2105.00414] Predicting highly correlated hydride-ion diffusion in SrTiO$ 3$ crystals based on the fragment kinetic Monte...2105.00414] Predicting highly correlated hydride-ion diffusion in SrTiO$ 3$ crystals based on the fragment kinetic Monte...
... crystals based on the fragment kinetic Monte Carlo method with machine-learning potential. Authors: Hiroya Nakata ... In this study, we investigate the hydride-ion migration mechanism using a kinetic Monte Carlo approach to understanding the ... The difficulties in applying the method to hydride and oxygen ion migration involve complex changes in the ionic migration ... The parallel-processing efficiency of our proposed method was 84.92 \% for our 1,000-CPU implementation, suggesting that the ...
http://arxiv-export-lb.library.cornell.edu/abs/2105.00414
Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method<...Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method<...
Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method. / Sadi, T.; Kelsall, R.; ... title = "Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method", ... Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method. ... T1 - Simulation of Electron Transport in InGaAs/AlGaAs HEMTs Using an Electrothermal Monte Carlo Method ...
https://abdn.pure.elsevier.com/en/publications/simulation-of-electron-transport-in-ingaasalgaas-hemts-using-an-e
Monte Carlo Method Archives - OLECDMonte Carlo Method Archives - OLECD
The role of an Analyst is dynamic, complex, and driven by a variety of skills. These skills range from a basic understanding of financial statement data and non-financial metrics that can be linked to financial performance, to a deeper dive into business and financial modeling ...
https://www.olecde.com/product-tag/monte-carlo-method/?products-per-page=24
Archiwa: Monte Carlo method - Agile InstituteArchiwa: Monte Carlo method - Agile Institute
Monte Carlo method Monte Carlo method (Metoda Monte Carlo): metoda modelowania matematycznego wykorzystująca losowe wielkości ...
https://agileinstitute.pl/index.php/encyclopedia/tag/monte-carlo-method/
Information for Monte Carlo Methods - AstroBakiInformation for "Monte Carlo Methods" - AstroBaki
Browsing by Subject Monte-Carlo methodBrowsing by Subject "Monte-Carlo method"
Swaption pricing under Libor market model using Monte-Carlo method with simulated annealing optimization  Ondieki, Kennedy ... Browsing by Subject "Monte-Carlo method". 0-9. A. B. C. D. E. F. G. H. I. J. K. L. M. N. O. P. Q. R. S. T. U. V. W. X. Y. Z. * ...
https://su-plus.strathmore.edu/browse?type=subject&value=Monte-Carlo+method
mVMC | many-variable variational Monte Carlo methodmVMC | many-variable variational Monte Carlo method
Project for advancement of software usability in materials science(PASUMS)
https://www.pasums.issp.u-tokyo.ac.jp/mvmc/en/
Introducing Monte Carlo Methods with R | EpiToDateIntroducing Monte Carlo Methods with R | EpiToDate
Introducing Monte Carlo Methods with R. Author: Robert, Casella Category: bayesian, biostatistics, r Publisher: Springer Verlag ...
https://epitodate.com/books/introducing-monte-carlo-methods-with-r/
SimulationsMarkov Chain Monte CQuasi-Monte Carlo mEconometricsAlgorithmIntroducing Monte Carlo MAdvanced MCMC methodsEstimateMCMCStochastic approximationDeterministicMultilevelBayesianUncertaintyStatisticalProbabilisticNumerical methodsOptimizationRegressionCalculationNumericallyAdaptiveParallelSmall Area EstimAlgorithmsTransport SimulationAnalysesSamplesComputational MethodsProhibitivelyQuantitativeEfficiencyMethodologyClassificationLatticePhysicsEstimation methodDynamics2021MethodologicalVarianceRadiationStudyApplicabilitySimulation-basedPredictionParameterAnalysisMathematicsDataIntroductionApproachesFrameworkRandomWorkshopCalculateParis
Simulations7
  • This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. (pasadena.si)
  • The parallel-processing efficiency of our proposed method was 84.92 \% for our 1,000-CPU implementation, suggesting that the approach should be widely applicable to simulations of ionic migration in crystals at a reasonable computational cost. (cornell.edu)
  • Monte Carlo simulations are implemented to investigate the probability of low electric fields driving electrons to runaway state. (unb.br)
  • Markov chain Monte Carlo simulations allow researchers to approximate posterior distributions that cannot be directly calculated. (cdc.gov)
  • The Monte Carlo simulations provided in this article show that our estimators perform well. (accessecon.com)
  • To validate the applicability of TIGRE to pCT, several Monte Carlo simulations modeling a pCT setup with two Catphan® modules as phantoms were performed. (physicamedica.com)
  • Using a combination of analytic methods and Monte Carlo simulations, we study the morphology of growing steps in detail. (aalto.fi)
Markov Chain Monte C1
Quasi-Monte Carlo m1
  • Eighth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Montreal, July 2008. (imag.fr)
Econometrics1
  • These include, for example, methods of econometrics or Monte-Carlo techniques and computer-intensive procedures on the one hand and special courses in survey statistics on the other. (uni-trier.de)
Algorithm7
  • A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants. (pasadena.si)
  • The Cooperative Motion Algorithm is an efficient lattice method to simulate dense polymer systems and is often used with two different criteria to generate a Markov chain in the configuration space. (iospress.com)
  • While the first method is the well-established Metropolis algorithm, the other one is an heuristic a lgorithm which needs justification. (iospress.com)
  • The same algorithm implemented using pepper.js can be found on the Monte Carlo Estimate for Pi site. (curiosity-driven.org)
  • Most of this lesson's content is devoted to studying the ray-marching algorithm but you should know that while this algorithm was used almost exclusively for volume rendering until relatively recently (the mid-2010s at least), modern rendering engines are now generally using a stochastic Monte-Carlo based approach with it comes to rendering volumes instead. (scratchapixel.com)
  • For historical reasons and because it's much easier to get introduced to the topic of volume rendering (and the Volume Rendering Equation) through the ray-marching algorithm than through stochastic methods that are significantly more complex (particularly from the point of view of someone new to CGI programming with little to no math background). (scratchapixel.com)
  • At the end of the module, you will be able to formalise a ML task, choose the appropriate method to process it numerically, implement the ML algorithm in Python, and assess the method¿s performance. (qmul.ac.uk)
Introducing Monte Carlo M4
  • Here are two more typos in the exercises of Chapter 3 of "Introducing Monte Carlo Methods with R". - due to the (later) inclusion of an extra-exercise in the book, the "above exercise" in Exercise 3.5 actually means Exercise 3.3. (r-bloggers.com)
  • When grading homeworks for my Monte Carlo graduate class, I found that my students had pointed out two typos in the exercises of Chapter 2 of "Introducing Monte Carlo Methods with R". - In Exercise 2.17, question d. should be "d. (r-bloggers.com)
  • Introducing Monte Carlo Methods with R" is out, truly out, I have received a copy from Springer by express mail today! (r-bloggers.com)
  • Following a proposal by Springer-Verlag Paris, I have decided to translate Introducing Monte Carlo Methods with R with George Casella into French, since a new collection of R books (in French) is planed for the Spring of 2010. (r-bloggers.com)
Advanced MCMC methods1
  • We will then discuss more advanced MCMC methods such as adaptive MCMC methods and auxiliary variable methods such as parallel tempering, particle MCMC methods and slice sampling. (videolectures.net)
Estimate9
  • In this paper, we develop methods to apply QMC and MLMC to EVPPI, addressing particular challenges that arise where Markov Chain Monte Carlo (MCMC) has been used to estimate input parameter distributions. (bris.ac.uk)
  • We can estimate the value of pi with random number by Monte Carlo method. (mathworks.com)
  • Instead, Monte Carlo simulation is used to estimate the lattice value by sampling appropriately from the nodes. (joim.com)
  • In RL Monte Carlo methods allow us to estimate values directly from experience, from sequences of states, actions and rewards. (coursera.org)
  • By the end of this video you will be able to understand how Monte Carlo methods can be used to estimate value functions from sampled interaction and identify problems that can be solved using Monte Carlo methods. (coursera.org)
  • Monte Carlo (named for the casino in Monaco) methods estimate a distribution by random sampling. (cdc.gov)
  • Abstract use this method to estimate the size of local populations eligible for, or in need of, preventive health services and Introduction interventions. (cdc.gov)
  • This study aims to estimate the lower limits for feasible Ra-223 single-photon emission computerized tomography (SPECT) imaging using a Monte Carlo simulation study .Methods:The SPECT images were produced on a homemade code: the Monte Carlo simulation of electrons and photons for SPECT (MCEP-SPECT). (kyushu-u.ac.jp)
  • This paper revisits the Two-Sample Two-Stage Least Squares (TSTSLS) method, which is commonly used to estimate intergenerational mobility in the absence of parental earnings data. (ssrn.com)
MCMC5
  • We will first review the Monte Carlo principle and standard Monte Carlo methods including rejection sampling, importance sampling and standard Markov chain Monte Carlo (MCMC) methods. (videolectures.net)
  • Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. (pasadena.si)
  • We illustrate the methods using a two examples: a simplified decision tree model for treatments for depression, and a complex Markov model for treatments to prevent stroke in atrial fibrillation, both of which use MCMC inputs. (bris.ac.uk)
  • A Markov chain Monte Carlo (MCMC) simulation is a method of estimating an unknown probability distribution for the outcome of a complex process (a posterior distribution). (cdc.gov)
  • We will discuss the MCMC method. (tifr.res.in)
Stochastic approximation2
  • Key Features: Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems. (pasadena.si)
  • Mini-symposium on Stochastic Approximation at the International Conference on Monte Carlo techniques , 2016, France, Paris. (imag.fr)
Deterministic3
  • As described in detail in [ 2 ], the B 1 MC method generates the homogenized few-group constants of fuel assemblies or fuel bundles in much the same way as its deterministic counterparts [ 3 - 5 ]. (hindawi.com)
  • Because of these characteristics of treating the nuclear cross section and geometrical data input exactly, the B 1 MC method may perform inherently more exact IMS calculations, which in turn makes subsequent CS calculations conducted on more precise multi-group B 1 equations, than its deterministic counterparts. (hindawi.com)
  • Deterministic and Stochastic (Monte-Carlo) based design methods for radiation shielding and protection. (tum.de)
Multilevel5
  • In this paper, we explore the potential of Quasi Monte-Carlo (QMC) and Multilevel Monte-Carlo (MLMC) estimation to reduce computational cost of estimating EVPPI by reducing the variance compared with MC, while preserving accuracy. (bris.ac.uk)
  • Multilevel approaches, such as multilevel Monte Carlo (MLMC), can reduce the complexity significantly when they are combined with a fast multigrid solver, but to exploit them fully in a parallel environment, sophisticated scheduling strategies are needed. (drzisga.dev)
  • Mini-symposium on Multilevel Monte Carlo Methods at the SMAI Congrès 2015, France, Savoie. (imag.fr)
  • Recently, considerable research has been done on multilevel, model-based, small-area estimation methods ( 10 , 13 - 15 ). (cdc.gov)
  • These methods can produce data on variations in the multilevel influence of local social and physical environments on health outcomes among people in small areas by using various demographic characteristics (eg, age, sex, race/ethnicity). (cdc.gov)
Bayesian1
  • In Bayesian inference, the model space is directly probed by sampling methods such that we obtain a reliable uncertainty appraisal, determine optimal models, and avoid entrapment in a small local region of the model space. (nsf.gov)
Uncertainty6
  • 2008). Evaluation of measurement data - Supplement 1 to the 'Guide to the expression of uncertainty in measurement' - Propagation of distributions using a Monte Carlo method, 1st Edition. (sciendo.com)
  • Comparison of the GUM and Monte Carlo methods for the uncertainty estimation in electromagnetic compatibility testing. (sciendo.com)
  • This discussion paper describes the methods used to quantify uncertainty in the various inputs to LE and HALE estimates, and how these uncertainties were incorporated in intervals around the reported measures. (who.int)
  • Modern epidemiological techniques report confidence or uncertainty intervals around all estimates, and there is a growing literature on methods for capturing uncertainty in quantitative policy analyses (see, for example, Morgan and Henrion (7) , King et al. (who.int)
  • For some simple quantities of interest, such as linear combinations of normally distributed random variables, uncertainty may be reasonably captured using analytic methods. (who.int)
  • We describe the methods used to combine the uncertainty from each input into the computations of HALE, and end with a discussion of continuing work and future directions in this area. (who.int)
Statistical7
  • The method is often used to solve problems that are hard to determine exactly, when there is enough statistical data available. (buddygo.net)
  • The solution of such a statistical inverse method is completely described by the posterior distribution, which quantifies the distributions for parameters and inversion uncertainties. (nsf.gov)
  • This module begins by covering some of the essential theoretical notions of probability and the distributions of random variables which underpin statistical methods. (qmul.ac.uk)
  • C. H. Kapadi and R. L. Thomasson, "On Estimating the Parameter of the Truncated Geometric Distribution by the Method of Moments," Annals of the Institute of Statistical Mathematics, Vol. 20, 1975, pp. 519-532. (scirp.org)
  • JD Felske,, Reviewer 15 01 2003 radiative transfer statistical analysis Monte Carlo methods ray tracing reviews 1R48. (asme.org)
  • Besides, the analysis and statistical modelling, the Logistic Regression method was used. (preprints.org)
  • The use of statistical and mathematical methods to analyze biological observations and phenomena. (bvsalud.org)
Probabilistic3
  • In particular we present Monte Carlo schemes in the framework of probabilistic chains. (unict.it)
  • Variability in human response can be treated effec- tively using probabilistic methods. (cdc.gov)
  • METHODS: We estimated the incremental number of pneumococcal disease events and deaths averted, costs per quality adjusted life-year (QALY) gained, and costs per life-year gained under different vaccination strategies using a probabilistic model following a single birth cohort of 3.9 million individuals (based on 2020 U.S. birth cohort). (cdc.gov)
Numerical methods4
  • The procedure offers a great advantage over the analytic solution of the problem and usual shell numerical methods when one wants to determine the dome geometry with constant stresses, since the presented procedure has the goal stress as input value for obtaining the geometry, as opposed to the usual numerical methods, where the reverse occurs. (scite.ai)
  • Third Conference on Numerical Methods in Finance, Paris, April 2009. (imag.fr)
  • Conference on Numerical Methods in Finance, Udine (Italy), June 2008. (imag.fr)
  • The modelling of structures and processes at these vastly different scales requires rather different theoretical and numerical methods, from quantum chemistry methods on the atomic scale to continuum models on the macroscopic scale. (psi-k.net)
Optimization3
  • Additionally, local optimization-based FWI methods cannot always converge to a geologically meaningful solution unless the inversion starts with an accurate background model. (nsf.gov)
  • We demonstrate the potential of this new computational framework by performing a linear coseismic slip inversion through adjoint-based optimization methods, without requiring computation of elastic Green's functions. (nsf.gov)
  • This feature makes unsuitable of traditional methods for optimization of the adequacy of electric power system. (e3s-conferences.org)
Regression4
  • The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. (mdpi.com)
  • Chaining methods have often providing state of the art results, and the idea of extending it to multi-output regression has already been trialed. (unict.it)
  • This module will introduce you to some of the most widely-used techniques in machine learning (ML). After reviewing the necessary background mathematics, we will investigate various ML methods, such as linear regression, polynomial regression and classification with logistic regression. (qmul.ac.uk)
  • Assessing meta-regression methods for examining moderator relationships with dependent effect sizes: A Monte Carlo simulation. (bvsalud.org)
Calculation3
  • L. Teich, C. Schröder, C. Müller, A. V. Patel, J. Meyer, and A. Hütten, “Efficient Calculation of Low Energy Configurations of Nanoparticle Ensembles for Magnetoresistive Sensor Devices by means of Stochastic Spin Dynamics and Monte Carlo Methods,” presented at the The European Conference Physics on Magnetism, Poznan, 2014. (fh-bielefeld.de)
  • Teich L, Schröder C, Müller C, Patel AV, Meyer J, Hütten A. Efficient Calculation of Low Energy Configurations of Nanoparticle Ensembles for Magnetoresistive Sensor Devices by means of Stochastic Spin Dynamics and Monte Carlo Methods. (fh-bielefeld.de)
  • In the calculation, the general method with the linear creep theory was applied. (scite.ai)
Numerically2
  • We then translate these results into large-time asymptotics for call options and implied volatility and we verify our results numerically using Monte Carlo simulation. (zbmath.org)
  • This article examines numerically the flexibility influence of support beams in static response and dynamic properties of a symmetric plate formed by massive slabs of reinforced concrete in elastic linear regime, using the Finite Element Method. (scite.ai)
Adaptive1
  • Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties. (sciendo.com)
Parallel1
  • From a computational perspective, my research interests have focused on Markov chain Monte Carlo and parallel computing methods in computational statistics. (osu.edu)
Small Area Estim1
  • Another advantage is that model-based small-area estimation methods borrow information from both individual-level data within the survey sample and from area-level covariates external to the original sample, and they tend to generate smoothed estimates with better precision ( 16 ). (cdc.gov)
Algorithms1
  • We use randomized quasi-Monte Carlo (RQMC) techniques to construct computational tools for working with normal mixture models, which include automatic integration routines for density and distribution function evaluation, as well as fitting algorithms. (uwaterloo.ca)
Transport Simulation1
  • The method presented in this paper uses a detailed Monte Carlo radiation transport simulation in one step from source to dose rate. (ans.org)
Analyses1
  • The applicability of the B 1 MC method to PWR core analyses has been demonstrated by showing that few-group constants from the method implemented in a Seoul National University (SNU) MC code, McCARD [ 6 ] lead to nodal core neutronics calculations in a good agreement with whole PWR core reference MC calculations [ 2 ]. (hindawi.com)
Samples3
  • We optimize the concurrent execution across the three layers of the MLMC method: parallelization across levels, across samples, and across the spatial grid. (drzisga.dev)
  • With this study, we aimed to validate an intra-laboratory molecular detection method for SARS-CoV-2 on saliva samples collected in a new storage saline solution, comparing the results to NP swabs to determine the difference in sensitivity between the two tests. (bvsalud.org)
  • Internal method validation was performed to standardize the entire workflow for saliva samples. (bvsalud.org)
Computational Methods1
  • The conference aims at bringing together researchers and practitioners to discuss recent developments in computational methods, methodology for data analysis and applications in statistics. (compstat2016.org)
Prohibitively1
  • Use of the surveillance methods for obtaining national (ie, large-area) data to obtain small-area data on childhood obesity is prohibitively expensive. (cdc.gov)
Quantitative1
  • The aim of this work was to assess the quantitative performance of a fully 3D Monte Carlo (MC) reconstruction for preclinical I-124 PET. (inserm.fr)
Efficiency2
  • They preserve and maintain stationary data structure of the process and are reliable for predictive purposes, confirming the efficiency of the proposed method. (scirp.org)
  • Furthermore, brushing has the same efficiency as a chemical method for biofilm removal 14 . (bvsalud.org)
Methodology1
  • Second, we use administrative data from a developing country to test our lower bound methodology through an Empirical Monte Carlo approach, confirming its validity. (ssrn.com)
Classification1
  • Cartographic classification methods present a simplified way to examine some classification and clustering methods, and these will be explored in more depth with example applications. (ucgis.org)
Lattice2
  • This paper presents a Markov chain Monte Carlo scheme, adapted from Sinclair and Jerrum (Information and Computation 82 (1989)), that is able to overcome this problem, provided some partial (possibly very inaccurate) information about the lattice sum is available. (joim.com)
  • The critical temperature, which was determined using the Binder cumulant, was the same for all methods with the simulation accuracy and in agreement with the exact critical temperature for the Ising model on the 2D triangular lattice. (iospress.com)
Physics4
  • Whenever possible, we draw connections between image-synthesis techniques and methods from particle physics and neutron transport to provide the reader with a broader context. (disneyresearch.com)
  • We review the main software and computing challenges for the Monte Carlo physics event generators used by the LHC experiments, in view of the High-Luminosity LHC (HL-LHC) physics programme. (springer.com)
  • For ongoing commitment to developing physics in Africa through initiating the African School on Electronic Structure Methods and Applications and leadership in bringing together African physicists from across the continent to create a Pan-African physics communication vehicle. (aps.org)
  • During his career, he organized or co-organized multiple international conferences and workshops promoting computational physics in general and the applications and development of quantum Monte Carlo methods. (aps.org)
Estimation method3
  • The term Monte Carlo is often used more broadly for any estimation method that relies on repeated random sampling. (coursera.org)
  • Knutson K, Zhang W, Hispanic women varied more than did those of women of Tabnak F. Applying the smal -area estimation method to other races. (cdc.gov)
  • We used the smal -area estimation method to determine implemented locally, in counties or cities (2). (cdc.gov)
Dynamics1
  • J Zehnder,, Reviewer atmospheric movements meteorology modelling Navier-Stokes equations flow simulation finite difference methods geophysical fluid dynamics radiative transfer 9R53. (asme.org)
20211
  • METHODS: The Centers for Disease Control and Prevention collected data from health departments on workplace COVID-19 outbreaks from August through October 2021: the number of workplace outbreaks, by workplace setting, and the total number of cases among workers linked to these outbreaks. (cdc.gov)
Methodological3
  • In these modules, advanced methods of statistics in general and inferential statistics in particular are taught, as well as further methodological and content-related emphases. (uni-trier.de)
  • Method: Qualitative research using symbolic interactionism as a theoretical reference and narrative research as a methodological reference. (bvsalud.org)
  • Method: Study of qualitative approach, whose theoretical and methodological frameworks. (bvsalud.org)
Variance2
  • Doesn't it imply improved Monte-Carlo method has a higher variance? (talkstats.com)
  • This paper describes detailed far-field dose rate calculations performed for a proposed CISF using MAVRIC, the Monte Carlo radiation shielding sequence in SCALE 6.2.3, with automated variance reduction based on discrete ordinates calculations. (ans.org)
Radiation1
  • Basic Methods for Radiation Dose Calculations. (tum.de)
Study11
  • In this study, we investigate the hydride-ion migration mechanism using a kinetic Monte Carlo approach to understanding the relationship between the hydride and oxygen ions. (cornell.edu)
  • In this work, we propose an improved 3D Monte Carlo Potts (MCP) method to quantitatively study the relationship between non-uniform temperature fields and final grain morphologies. (techscience.com)
  • This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns. (ucgis.org)
  • We introduce for the first time the grand-canonical Monte Carlo method to study the growth/dissolution steps of nanoparticle growth mediated by seeds. (nau.edu)
  • The main purpose of this study is to present a new method for determining the situations in which, dispersion coefficient has significant effect on solute transport mechanism. (ac.ir)
  • 1996). However, these studies did value, this study provides an example of this method not attempt to derive guidance values. (cdc.gov)
  • Akihiko Takahashi, Hibiki Ueno, Shingo Baba, Masayuki Sasaki, Estimation of the lower limits for feasible Ra-223 SPECT imaging: a Monte Carlo simulation study, Asia Oceania Journal of Nuclear Medicine & Biology , 2021.06, Objective(s):Ra-223 is a promising radionuclide for the treatment of skeletal metastases in castration-resistant prostate cancer patients. (kyushu-u.ac.jp)
  • Method: This is a descriptive study with a qualitative approach, based on Merleau-Ponty's phenomenology and performed at the Fernando Magalhães Maternity Hospital in the state of Rio de Janeiro, through interviews wi. (bvsalud.org)
  • Method: a cross-sectional study based on records data of preterm newborns hospitalized in two neonatal units in northwest Paraná State in 2015. (bvsalud.org)
  • Method: Exploratory, qualitative study conducted at a Neonatal Intensive Care Unit from a University Hospital in the state capital of southern B. (bvsalud.org)
  • Method: a qualitative study with 11 nurses from Neonatal Intensive Care Units of a city in the State of São Paulo countryside, based on the Developmental Care. (bvsalud.org)
Applicability1
  • To examine the applicability of the B 1 MC method for the CANDU 6 core analysis, the fuel bundle cell and supercell calculations are performed using it to obtain the two-group constants. (hindawi.com)
Simulation-based1
  • Methods A gamma camera simulation based on the Monte Carlo method was performed to produce SPECT images. (kyushu-u.ac.jp)
Prediction1
  • You will learn about on-policy and off-policy methods for prediction and control, using Monte Carlo methods---methods that use sampled returns. (coursera.org)
Parameter1
  • We consider Monte Carlo sampling methods for parameter estimation and inference under nonlinear and non-Gaussian assumptions. (cdc.gov)
Analysis6
  • An introduction of simulation utilizing the Monte Carlo method of analysis as a tool for U.S. Coast Guard personnel administration. (nps.edu)
  • As an alternative way to generate the two-group constants necessary for the CANDU-PHWR core analysis, this paper proposes utilizing a B 1 theory augmented Monte Carlo (MC) few-group constant generation method (B 1 MC method) which has been devised for the PWR fuel assembly analysis method. (hindawi.com)
  • The purpose of this paper is to demonstrate that the B 1 MC method is also applicable to the neutronics analysis of CANDU-PHWRs by showing that homogenized two-group constants of fuel bundles of CANDU 6 from it can result in core neutronics calculations that agree very well with a reference CANDU 6 whole core analysis. (hindawi.com)
  • For analysis purposes, the Monte Carlo method was adopted, followed by the First Order Reliability Method (FORM). (scite.ai)
  • The reliability analysis of the variants of development of electric power system is carried out on the base of the Monte Carlo method. (e3s-conferences.org)
  • Monte Carlo analysis of impact of underascertainment of mesothelioma cases on underestimation of risk. (cdc.gov)
Mathematics1
  • T. Olatayo, "On the Application of Bootstrap Method to Stationary Time Series Process," American Journal of Computational Mathematics , Vol. 3 No. 1, 2013, pp. 61-65. (scirp.org)
Data5
  • However, discrepancies exist among these models due to differences in both data sets and tomographic methods, which introduce uncertainties on the imaged lithospheic structure beneath North America. (nsf.gov)
  • A further model refinement using the best data coverage and advanced tomographic methods such as full-waveform inversion (FWI) is expected to provide better seismological constraints. (nsf.gov)
  • The performance of the different methods is studied on computer-simulated and controlled laboratory-generated data. (cdc.gov)
  • There are, however, cost-effective methods of generating health-related data, particularly on obesity, for small-area populations ( 10 - 12 ). (cdc.gov)
  • A structured self-report data collection method was applied to elicit data about the partici- pants' knowledge and circumstances that prevent them from reporting child abuse. (who.int)
Introduction2
  • Hamra G , MacLehose R , Richardson D . Markov chain Monte Carlo: an introduction for epidemiologists. (cdc.gov)
  • A simple introduction to Markov Chain Monte-Carlo sampling. (cdc.gov)
Approaches1
  • Thankfully with the continuous rise in computing power, we can now produce results with the stochastic method within reasonable times, and since it creates better results, the ray-marching method has been phased out in favor of stochastic-based approaches instead. (scratchapixel.com)
Framework1
  • SUMMARY We introduce a new finite-element (FE) based computational framework to solve forward and inverse elastic deformation problems for earthquake faulting via the adjoint method. (nsf.gov)
Random2
  • We explore how we can still use RQMC in this setting and propose several methods when sampling of W is only possible via a black box random variate generator. (uwaterloo.ca)
  • Recall the Box-Muller method which transforms pairs of uniformly-distributed random variables to obtain a pair of independent standard normal random variates. (warwick.ac.uk)
Workshop1
  • Invited to the Workshop on Monte Carlo Methods, Ecole Polytechnique, October 2012. (imag.fr)
Calculate1
  • The one notable difference is that, unlike the model used to calculate national level estimates, the software does not use Monte-Carlo methodologies to provide ranges of estimates. (cdc.gov)
Paris1
  • International Conference on Monte Carlo techniques , Paris, July 2016. (imag.fr)
Water | Free Full-Text | Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current...
Water | Free Full-Text | Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current... (mdpi.com)
Energies | Free Full-Text | 3DHIP-Calculator-A New Tool to Stochastically Assess Deep Geothermal Potential Using the Heat-In...
Energies | Free Full-Text | 3DHIP-Calculator-A New Tool to Stochastically Assess Deep Geothermal Potential Using the Heat-In... (mdpi.com)
Monte Carlo Few-Group Constant Generation for CANDU 6 Core Analysis
Monte Carlo Few-Group Constant Generation for CANDU 6 Core Analysis (hindawi.com)
Response surface methodology with stochastic constraints for expensive simulation | Semantic Scholar
Response surface methodology with stochastic constraints for expensive simulation | Semantic Scholar (semanticscholar.org)
Journal of Machine Learning Research
Journal of Machine Learning Research (jmlr.org)
Molecular Modeling | BSC-CNS
Molecular Modeling | BSC-CNS (bsc.es)
Brian Phillip Weaver | Find Expertise
Brian Phillip Weaver | Find Expertise (lanl.gov)
Center for Computing Research (CCR)
Center for Computing Research (CCR) (sandia.gov)
CEEOL - Browse Subjects Result
CEEOL - Browse Subjects Result (ceeol.com)
Libros de Empresa | Seguros y reaseguros | Obras Generales · Marcial Pons Librero
Libros de Empresa | Seguros y reaseguros | Obras Generales · Marcial Pons Librero (marcialpons.es)
Computer Modeling in Engineering & Sciences | Simulating the Effect of Temperature Gradient on Grain Growth of 6061-T6 Aluminum...
Computer Modeling in Engineering & Sciences | Simulating the Effect of Temperature Gradient on Grain Growth of 6061-T6 Aluminum... (techscience.com)
General Post Archives - ElegantCode
General Post Archives - ElegantCode (elegantcode.com)
SAS Analytics Pro Features | SAS
SAS Analytics Pro Features | SAS (sas.com)
Nonlife actuarial models theory methods and evaluation | Statistics for econometrics, finance and insurance | Cambridge...
Nonlife actuarial models theory methods and evaluation | Statistics for econometrics, finance and insurance | Cambridge... (cambridge.org)
Preventing Chronic Disease | A Multilevel Approach to Estimating Small Area Childhood Obesity Prevalence at the Census Block...
Preventing Chronic Disease | A Multilevel Approach to Estimating Small Area Childhood Obesity Prevalence at the Census Block... (cdc.gov)
Introducing Monte Carlo Methods with R | R-bloggers - Part 3
Introducing Monte Carlo Methods with R | R-bloggers - Part 3 (r-bloggers.com)
Geometric Brownian motion (GBM) model - MATLAB
Geometric Brownian motion (GBM) model - MATLAB (mathworks.com)
Harald Niederreiter
Harald Niederreiter (oeaw.ac.at)
Frontiers | Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using...
Frontiers | Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using... (frontiersin.org)
Progress in Reducing Measles Mortality --- Worldwide, 1999--2003
Progress in Reducing Measles Mortality --- Worldwide, 1999--2003 (cdc.gov)
Progress in Reducing Global Measles Deaths, 1999--2004
Progress in Reducing Global Measles Deaths, 1999--2004 (cdc.gov)
Lukas Petrich - Universität Ulm
Lukas Petrich - Universität Ulm (uni-ulm.de)
Inverse renormalization group based on image super-resolution using deep convolutional networks | Scientific Reports
Inverse renormalization group based on image super-resolution using deep convolutional networks | Scientific Reports (nature.com)
GNUマニュアル・オンライン - GNUプロジェクト - フリーソフトウェアファウンデーション
GNUマニュアル・オンライン - GNUプロジェクト - フリーソフトウェアファウンデーション (gnu.org)
Distant Relatives of Severe Acute Respiratory Syndrome Coronavirus and Close Relatives of Human Coronavirus 229E in Bats, Ghana...
Distant Relatives of Severe Acute Respiratory Syndrome Coronavirus and Close Relatives of Human Coronavirus 229E in Bats, Ghana... (wwwnc.cdc.gov)
Comparison of Syndromic Surveillance and a Sentinel Provider System in Detecting an Influenza Outbreak --- Denver, Colorado,...
Comparison of Syndromic Surveillance and a Sentinel Provider System in Detecting an Influenza Outbreak --- Denver, Colorado,... (cdc.gov)
Events by date | Statistics and Actuarial Science | University of Waterloo
Events by date | Statistics and Actuarial Science | University of Waterloo (uwaterloo.ca)
Improved sampling strategies for ensemble-based optimization | SpringerLink
Improved sampling strategies for ensemble-based optimization | SpringerLink (link.springer.com)
Understanding hydrogen sorption in a polar metal-organic framework with constricted channels - NASA/ADS
Understanding hydrogen sorption in a polar metal-organic framework with constricted channels - NASA/ADS (ui.adsabs.harvard.edu)
SSP 2012 : IEEE Statistical Signal Processing Workshop
SSP 2012 : IEEE Statistical Signal Processing Workshop (wikicfp.com)