On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)

Clicks: 3
ID: 291168
1997
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Ranked #100 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
Summary New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.
Reference Key
openalex_W2038885294 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sylvia Richardson, Peter J. Green
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 1997
DOI
10.1111/1467-9868.00095
URL
https://academic.oup.com/jrsssb/article-pdf/59/4/731/49588858/jrsssb_59_4_731.pdf
Keywords Keywords not found

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