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Marginal Likelihood Estimation with the Cross-Entropy Method

Chan, Chi Chun (Joshua); Eisenstat, Eric


We consider an adaptive importance sampling approach to estimating the marginal likelihood, a quantity that is fundamental in Bayesian model comparison and Bayesian model averaging. This approach is motivated by the difficulty of obtaining an accurate estimate through existing algorithms that use Markov chain Monte Carlo (MCMC) draws, where the draws are typically costly to obtain and highly correlated in high-dimensional settings. In contrast, we use the cross-entropy (CE) method, a versatile...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Journal article
Source: Econometric Reviews
DOI: 10.1080/07474938.2014.944474


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