Results 11 to 20 of about 112,632 (333)
Event-Chain Monte-Carlo Simulations of Dense Soft Matter Systems
Frontiers in Physics, 2021 We discuss the rejection-free event-chain Monte-Carlo algorithm and several applications to dense soft matter systems. Event-chain Monte-Carlo is an alternative to standard local Markov-chain Monte-Carlo schemes, which are based on detailed balance, for ...
Tobias Alexander Kampmann +4 more
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Applying diffusion-based Markov chain Monte Carlo. [PDF]
PLoS ONE, 2017 We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC.
Radu Herbei, Rajib Paul, L Mark Berliner
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Population Markov Chain Monte Carlo [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Laskey, Kathryn Blackmond +1 more
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Towards derandomising Markov chain Monte Carlo
2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS), 2023 We present a new framework to derandomise certain Markov chain Monte Carlo (MCMC) algorithms. As in MCMC, we first reduce counting problems to sampling from a sequence of marginal distributions. For the latter task, we introduce a method called coupling towards the past that can, in logarithmic time, evaluate one or a constant number of variables from ...
Feng, Weiming +4 more
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Multilevel Markov Chain Monte Carlo [PDF]
SIAM Review, 2019 The authors are interested in uncertainty quantification in porous media flow with high-dimensional parameter spaces. This problem is often solved by Markov chain Monte Carlo methods, which have a prohibitively large computational cost. First, the authors propose a new multilevel Metropolis-Hastings algorithm and establish a complexity theorem that ...
Dodwell, T +3 more
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Handbook of Markov Chain Monte Carlo [PDF]
, 2011 Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella Reversible
Radford M. Neal
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A Markov chain Monte Carlo method family in incomplete data analysis [PDF]
Ekonomski Anali, 2003 A Markov chain Monte Carlo method family is a collection of techniques for pseudorandom draws out of probability distribution function. In recent years, these techniques have been the subject of intensive interest of many statisticians. Roughly speaking,
Vasić Vladimir V.
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Advances in Mechanical Engineering, 2022 Aiming at the contact strength reliability of variable hyperbolic circular arc gear, a reliability analysis method for contact strength of variable hyperbolic circular arc gear based on Kriging model and advanced first-order and second-moment algorithm ...
Zhang Qi +6 more
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Genetics Selection Evolution, 2012 Background Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques.
Wu Xiao-Lin +6 more
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Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions
Entropy, 2014 Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond statistics. A full exposition of Markov chains and their use in Monte Carlo
Samuel Livingstone, Mark Girolami
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