Results 11 to 20 of about 85,479 (297)
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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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 ...
Tim J. Dodwell +3 more
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Population Markov Chain Monte Carlo [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kathryn B. Laskey, James W. Myers
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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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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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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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Coreset Markov Chain Monte Carlo
, 2023 A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during inference in order to reduce computational cost. However, state of the art methods for tuning coreset weights are expensive, require nontrivial user input, and impose constraints on the model.
Naitong Chen, Trevor Campbell
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Putting Markov Chains Back into Markov Chain Monte Carlo [PDF]
Journal of Applied Mathematics and Decision Sciences, 2007 Markov chain theory plays an important role in statistical inference both in the formulation of models for data and in the construction of efficient algorithms for inference. The use of Markov chains in modeling data has a long history, however the use of Markov chain theory in developing algorithms for statistical inference has only become popular ...
Richard J. Barker, Matthew R. Schofield
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