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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A quantum parallel Markov chain Monte Carlo. [PDF]
J Comput Graph Stat, 2023 We propose a novel hybrid quantum computing strategy for parallel MCMC algorithms that generate multiple proposals at each step. This strategy makes the rate-limiting step within parallel MCMC amenable to quantum parallelization by using the Gumbel-max trick to turn the generalized accept-reject step into a discrete optimization problem.
Holbrook AJ.
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Quantum annealing enhanced Markov-Chain Monte Carlo [PDF]
Scientific ReportsIn this study, we propose quantum annealing-enhanced Markov Chain Monte Carlo (QAEMCMC), where QA is integrated into the MCMC subroutine. QA efficiently explores low-energy configurations and overcomes local minima, enabling the generation of proposal ...
Shunta Arai, Tadashi Kadowaki
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Markov chain Monte Carlo for active module identification problem [PDF]
BMC Bioinformatics, 2020 Background Integrative network methods are commonly used for interpretation of high-throughput experimental biological data: transcriptomics, proteomics, metabolomics and others.
Nikita Alexeev +4 more
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MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling [PDF]
BMC Bioinformatics, 2012 Background Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly.
Kitchen James L +3 more
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Posterior-based proposals for speeding up Markov chain Monte Carlo [PDF]
Royal Society Open Science, 2019 Markov chain Monte Carlo (MCMC) is widely used for Bayesian inference in models of complex systems. Performance, however, is often unsatisfactory in models with many latent variables due to so-called poor mixing, necessitating the development of ...
C. M. Pooley +3 more
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Etymologia: Markov Chain Monte Carlo [PDF]
Emerging Infectious Diseases, 2019 Ronnie Henry
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MCMCpack: Markov Chain Monte Carlo in R
Journal of Statistical Software, 2011 We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. In addition to code that can be used to fit commonly used models, MCMCpack also contains some useful ...
Andrew D. Martin +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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Multiparameter Approximation Model of Temperature Conditions of Marine Diesel Generator Sets, Based on Markov Chain Monte Carlo [PDF]
TransNav, 2022 In the article we propose a multi-parameter approximation model, based on Markov chain Monte Carlo, which describes the relationship between the temperature regime, operating conditions and electromechanical parameters of marine diesel generator sets ...
Volodymyr Myrhorod +2 more
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