An asymptotic Peskun ordering and its application to lifted samplers

DOI10.3150/23-bej1674MaRDI QIDQ6565323

Philippe Gagnon, Florian Maire

Publication date: 2 July 2024

Published in: Bernoulli (Search for Journal in Brave)

zbMATH Keywords

Ising modelBayesian statisticsMarkov chain Monte Carlo methodsvariable selectionbinary random variables

Mathematics Subject Classification ID

Probability theory and stochastic processes (60-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Related Items (1)

Robust heavy-tailed versions of generalized linear models with applications in actuarial science

Cites Work

• Non-reversible Metropolis-Hastings
• A note on Metropolis-Hastings kernels for general state spaces
• Analysis of a nonreversible Markov chain sampler.
• The Bernstein-von Mises theorem under misspecification
• Peskun-Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario
• Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers
• Lifting Markov chains to speed up mixing
• Optimum Monte-Carlo sampling using Markov chains
• Asymptotic Statistics
• Nonreversible Jump Algorithms for Bayesian Nested Model Selection
• Informed Proposals for Local MCMC in Discrete Spaces
• Approximate Spectral Gaps for Markov Chain Mixing Times in High Dimensions
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• Non-Reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme
• The Barker Proposal: Combining Robustness and Efficiency in Gradient-Based MCMC
• Non-reversible guided Metropolis kernel

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