Markov Chain Importance Sampling—A Highly Efficient Estimator for MCMC

DOI10.1080/10618600.2020.1826953OpenAlexW3091028118MaRDI QIDQ5066382

Ilja Klebanov, Ingmar Schuster

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2020.1826953

zbMATH Keywords

Monte Carloimportance samplingvariance reductionproposal distributiondiscretized Langevinunadjusted Langevin

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review ⋮ On a Metropolis-Hastings importance sampling estimator ⋮ Gradient-based adaptive importance samplers ⋮ The importance Markov chain

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