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Independent and Identically Distributed Monte Carlo Algorithms for Semiparametric Linear Mixed Models
- JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2002
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Bayesian Model Selection in Finite Mixtures by Marginal Density Decompositions
- JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2001
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Some Further Developments for Stick-Breaking Priors: Finite and Infinite Clustering and Classification
- Sankhya Series A
, 2003
"... this paper will be to develop new surrounding theory for the hierarchical model (7) and show how these may be used to develop computational algorithms for computing posterior quantities. Our theoretical contributions include developing key properties for the class of extended stick-breaking measures ..."
Abstract
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this paper will be to develop new surrounding theory for the hierarchical model (7) and show how these may be used to develop computational algorithms for computing posterior quantities. Our theoretical contributions include developing key properties for the class of extended stick-breaking measures, which includes establishing a conjugacy property of their random weights to i.i.d sampling, and a characterization of the posterior for the extended stick-breaking prior under i.i.d sampling. See Section 3. These properties then lead us in Section 4 to a general characterization for the posterior of (7). In Section 5 we outline a collapsed Gibbs sampling algorithm and an i.i.d SIS (sequential importance sampling) algorithm that can be used for inference in (7). One important implication is our ability to t the posterior of (6) subject to in nite dimensional stick-breaking measures. The paper begins with a brief discussion of stick-breaking priors in Section 2
