Quotation Frühwirth-Schnatter, Sylvia. 2019. Keeping the balance—Bridge sampling for marginal likelihood estimation in finite mixture, mixture of experts and Markov mixture models. Brazilian Journal of Probability and Statistics. 33 (4), 706-733.


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Abstract

Finite mixture models and their extensions to Markov mixture and mixture of experts models are very popular in analysing data of various kind. A challenge for these models is choosing the number of components based on marginal likelihoods. The present paper suggests two innovative, generic bridge sampling estimators of the marginal likelihood that are based on constructing balanced importance densities from the conditional densities arising during Gibbs sampling. The full permutation bridge sampling estimator is derived from considering all possible permutations of the mixture labels for a subset of these densities. For the double random permutation bridge sampling estimator, two levels of random permutations are applied, first to permute the labels of the MCMC draws and second to randomly permute the labels of the conditional densities arising during Gibbs sampling. Various applications show very good performance of these estimators in comparison to importance and to reciprocal importance sampling estimators derived from the same importance densities.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Brazilian Journal of Probability and Statistics
Language English
Title Keeping the balance—Bridge sampling for marginal likelihood estimation in finite mixture, mixture of experts and Markov mixture models
Volume 33
Number 4
Year 2019
Page from 706
Page to 733
DOI http://dx.doi.org/10.1214/19-BJPS446
Open Access N

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Frühwirth-Schnatter, Sylvia (Details)
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Institute for Statistics and Mathematics IN (Details)
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