MCMC with the wrong acceptance probability
Read OriginalThis technical article examines what happens to Markov Chain Monte Carlo (MCMC) algorithms when the acceptance probability is computed incorrectly or approximated. It explains the Metropolis-Hastings framework, discusses the role of the acceptance probability, and reviews academic literature on the robustness of MCMC when using perturbed or unbiased random acceptance probabilities.
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