Likelihood-free Markov chain Monte Carlo
S A Sisson, Y Fan
TL;DR
This paper discusses likelihood-free Markov chain Monte Carlo methods, which enable Bayesian inference when likelihood functions are intractable, by using simulation-based approaches within the MCMC framework.
Contribution
It introduces or reviews likelihood-free MCMC techniques, expanding the toolkit for Bayesian inference in complex models with intractable likelihoods.
Findings
Likelihood-free MCMC allows inference without explicit likelihood calculations.
The methods are applicable to complex models where likelihoods are difficult to compute.
The paper provides a comprehensive overview of these techniques.
Abstract
To appear to MCMC handbook, S. P. Brooks, A. Gelman, G. Jones and X.-L. Meng (eds), Chapman & Hall.
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Statistical Methods and Inference · Bayesian Methods and Mixture Models