Results 11 to 20 of about 221,738 (314)
Parameter Estimation in Population Balance through Bayesian Technique Markov Chain Monte Carlo [PDF]
Journal of Applied and Computational Mechanics, 2021 In this work, the Markov Chain Monte Carlo is applied to estimate parameters that represent mechanisms that describe particles' dynamics in particulate systems from the literature's proposed models.
Carlos H.R. Moura +5 more
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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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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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Scalable Importance Tempering and Bayesian Variable Selection [PDF]
, 2019 We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling.
Roberts, Gareth, Zanella, Giacomo
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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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