A large deviation principle for the empirical measures of Metropolis–Hastings chains-Reference-Cited by-同舟云学术

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A large deviation principle for the empirical measures of Metropolis–Hastings chains

Published:2024-04 Issue: Volume:170 Page:104293
ISSN:0304-4149
Container-title:Stochastic Processes and their Applications
language:en
Short-container-title:Stochastic Processes and their Applications

Author:

Milinanni Federica^ORCID,Nyquist Pierre

Funder

Swedish e-Science Research Centre

Knut och Alice Wallenbergs Stiftelse

Vetenskapsrådet

Publisher

Elsevier BV

Reference49 articles.

1. An introduction to MCMC for machine learning;Andrieu;Mach. Learn.,2003

2. Explicit convergence bounds for Metropolis Markov chains: isoperimetry, spectral gaps and profiles;Andrieu,2022

3. Poincaré inequalities for Markov chains: a meeting with cheeger, Lyapunov and Metropolis;Andrieu,2022

4. Stochastic Simulation : Algorithms and Analysis;Asmussen,2007

5. Approximate Bayesian computation;Beaumont;Annu. Rev. Stat. Appl.,2019

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