Exact Bayesian inference via data augmentation

Neal, Peter and Kypraios, Theodore (2015) Exact Bayesian inference via data augmentation. Statistics and Computing, 25 (2). pp. 333-347. ISSN 0960-3174

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Abstract

Data augmentation is a common tool in Bayesian statistics, especially in the application of MCMC. Data augmentation is used where direct computation of the posterior density, π(θ|x), of the parameters θ, given the observed data x, is not possible. We show that for a range of problems, it is possible to augment the data by y, such that, π(θ|x,y) is known, and π(y|x) can easily be computed. In particular, π(y|x) is obtained by collapsing π(y,θ|x) through integrating out θ. This allows the exact computation of π(θ|x) as a mixture distribution without recourse to approximating methods such as MCMC. Useful byproducts of the exact posterior distribution are the marginal likelihood of the model and the exact predictive distribution.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Additional Information:
© The Author(s) 2013. This article is published with open access at Springerlink.com
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
73873
Deposited By:
Deposited On:
18 Jun 2015 05:55
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Sep 2020 03:19