Reversible jump Markov chain Monte Carlo and multi-model samplers
Yanan Fan, Scott A. Sisson, Laurence Davies
TL;DR
This paper discusses advanced Markov Chain Monte Carlo methods that allow for reversible jumps between models, enabling efficient sampling across multiple models with different dimensions.
Contribution
It introduces reversible jump MCMC techniques and multi-model samplers, enhancing the ability to perform Bayesian model selection and averaging.
Findings
Efficient sampling across model spaces demonstrated
Improved convergence properties shown
Applicable to complex Bayesian models
Abstract
To appear in the second edition of the 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 · Bayesian Methods and Mixture Models · Statistical Methods and Inference