Results 1 to 10 of about 112,632 (333)

Quantum annealing enhanced Markov-Chain Monte Carlo [PDF]

Scientific Reports
In 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
doaj   +2 more sources

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, Javlon Isomurodov, Vladimir Sukhov, Gennady Korotkevich, Alexey Sergushichev   +4 more
doaj   +2 more sources

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, S. C. Bishop, A. Doeschl-Wilson, G. Marion   +3 more
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A simple introduction to Markov Chain Monte-Carlo sampling. [PDF]

Psychon Bull Rev, 2018
Markov Chain Monte–Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It
van Ravenzwaaij D, Cassey P, Brown SD.
europepmc   +2 more sources

Etymologia: Markov Chain Monte Carlo [PDF]

Emerging Infectious Diseases, 2019
Ronnie Henry
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Stratification as a general variance reduction method for Markov chain Monte Carlo. [PDF]

SIAM/ASA J Uncertain Quantif, 2020
Dinner AR, Thiede EH, Van Koten B, Weare J.   +3 more
europepmc   +3 more sources

Identifiability and convergence behavior for Markov chain Monte Carlo using multivariate probit models. [PDF]

Commun Stat Theory Methods
Zhang X.
europepmc   +2 more sources

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, Eiji Ota, Hidetoshi Matsuo, Takaaki Matsunaga, Aki Miyazaki, Takamichi Murakami   +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, Rendani Mbuvha, Tshilidzi Marwala   +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, Bruno M. Viegas, Maria Tavares, Emanuel N. Macêdo, Diego C. Estumano, João N.N. Quaresma   +5 more
doaj   +1 more source