Topic
Monte Carlo molecular modeling
About: Monte Carlo molecular modeling is a research topic. Over the lifetime, 11307 publications have been published within this topic receiving 409122 citations.
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28 Mar 2016
TL;DR: In this article, the authors present the principles of Monte Carlo Methods, Girsanov's Theorem, and Stochastic Algorithms for Markov Processes with Jumps.
Abstract: Part I:Principles of Monte Carlo Methods.- 1.Introduction.- 2.Strong Law of Large Numbers and Monte Carlo Methods.- 3.Non Asymptotic Error Estimates for Monte Carlo Methods.- Part II:Exact and Approximate Simulation of Markov Processes.- 4.Poisson Processes.- 5.Discrete-Space Markov Processes.- 6.Continuous-Space Markov Processes with Jumps.- 7.Discretization of Stochastic Differential Equations.- Part III:Variance Reduction, Girsanov's Theorem, and Stochastic Algorithms.- 8.Variance Reduction and Stochastic Differential Equations.- 9.Stochastic Algorithms.- References.- Index.
73 citations
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01 Dec 1998TL;DR: The paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques and finds that through Latin supercube sampling it is possible to use Monte Carlo methods to extend quasi-monte Carlo Methods to higher dimensional problems.
Abstract: The paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques. Randomized quasi-Monte Carlo methods provide a basis for error estimation. They have, in the special case of scrambled nets, also been observed to improve accuracy. Finally, through Latin supercube sampling it is possible to use Monte Carlo methods to extend quasi-Monte Carlo methods to higher dimensional problems.
73 citations
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TL;DR: In this article, a simple approach is described to calculate sample-specific standard errors for the concentrations predicted by a three-way parallel factor (PARAFAC) analysis model, which involves a first-order error propagation equation in which the correct sensitivity and leverage values are introduced.
73 citations
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TL;DR: The theoretical validity of the proposed couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee (2014) is established and their efficiency relative to the underlying MCMC algorithms is studied.
Abstract: Markov chain Monte Carlo (MCMC) methods provide consistent of integrals as the number of iterations goes to infinity. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee (2014). The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretical validity of the proposed estimators and study their efficiency relative to the underlying MCMC algorithms. Finally, we illustrate the performance and limitations of the method on toy examples, on an Ising model around its critical temperature, on a high-dimensional variable selection problem, and on an approximation of the cut distribution arising in Bayesian inference for models made of multiple modules.
73 citations
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TL;DR: In this article, the basic components of Monte Carlo simulation of bremsstrahlung emission by electrons are presented and various theoretical cross-sections that have been used in Monte Carlo codes are described.
73 citations