Statistical Science

Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis

Ewan Cameron and Anthony Pettitt

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We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the (equivalent) algorithms known as “biased sampling” or “reverse logistic regression” in the statistics literature and “the density of states” in physics. Through a pair of numerical examples (including mixture modeling of the well-known galaxy data set) we highlight the remarkable diversity of sampling schemes amenable to such recursive normalization, as well as the notable efficiency of the resulting pseudo-mixture distributions for gauging prior sensitivity in the Bayesian model selection context. Our key theoretical contributions are to introduce a novel heuristic (“thermodynamic integration via importance sampling”) for qualifying the role of the bridging sequence in this procedure and to reveal various connections between these recursive estimators and the nested sampling technique.

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Statist. Sci., Volume 29, Number 3 (2014), 397-419.

First available in Project Euclid: 23 September 2014

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Bayes factor Bayesian model selection importance sampling marginal likelihood Metropolis-coupled Markov Chain Monte Carlo nested sampling normalizing constant path sampling reverse logistic regression thermodynamic integration


Cameron, Ewan; Pettitt, Anthony. Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis. Statist. Sci. 29 (2014), no. 3, 397--419. doi:10.1214/13-STS465.

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