Nonreversible Jump Algorithms for Bayesian Nested Model Selection

DOI10.1080/10618600.2020.1826955OpenAlexW3090563747MaRDI QIDQ5066387

Philippe Gagnon, Arnaud Doucet

Publication date: 29 March 2022

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

Full work available at URL: https://arxiv.org/abs/1911.01340

zbMATH Keywords

weak convergenceBayesian statisticsMarkov chain Monte Carlo methodsnonreversible Markov chainsPeskun-Tierney ordering

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Zig-Zag Sampling for Discrete Structures and Nonreversible Phylogenetic MCMC ⋮ Informed reversible jump algorithms

Cites Work

• Unnamed Item
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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Analysis of a nonreversible Markov chain sampler.
• Weak convergence and optimal tuning of the reversible jump algorithm
• Peskun-Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario
• Lifting Markov chains to speed up mixing
• Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes
• Weak convergence of a sequence of Markov chains
• Optimal Scaling of Discrete Approximations to Langevin Diffusions
• Efficient Construction of Reversible Jump Markov Chain Monte Carlo Proposal Distributions
• Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes
• Informed Proposals for Local MCMC in Discrete Spaces
• Fastest Mixing Markov Chain on a Path

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