Results 1 to 10 of about 215,368 (266)
Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions [PDF]
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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Statistical Inference for Partially Observed Markov Processes via the R Package pomp [PDF]
Journal of Statistical Software, 2016 Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis.
Aaron A. King +2 more
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Comparison between pystan and numpyro in Bayesian item response theory: evaluation of agreement of estimated latent parameters and sampling performance [PDF]
PeerJ Computer Science, 2023 Purpose The purpose of this study is to compare two libraries dedicated to the Markov chain Monte Carlo method: pystan and numpyro. In the comparison, we mainly focused on the agreement of estimated latent parameters and the performance of sampling using
Mizuho Nishio +5 more
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Antithetic Magnetic and Shadow Hamiltonian Monte Carlo
IEEE Access, 2021 Hamiltonian Monte Carlo is a Markov Chain Monte Carlo method that has been widely applied to numerous posterior inference problems within the machine learning literature. Markov Chain Monte Carlo estimators have higher variance than classical Monte Carlo
Wilson Tsakane Mongwe +2 more
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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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