Sampling Truncated Normal, Beta and Gamma Densities

Damien, Paul and Walker, Stephen G. (2001) Sampling Truncated Normal, Beta and Gamma Densities. Journal of Computational and Graphical Statistics, 10 (2). pp. 206-215. ISSN 1061-8600 . (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1198/10618600152627906

Abstract

We consider the Bayesian analysis of constrained parameter and truncated data problems within a Gibbs sampling framework and concentrate on sampling truncated densities that arise as full conditional densities within the context of the Gibbs sampler. In particular, we restrict attention to the normal, beta, and gamma densities. We demonstrate that, in many instances, it is possible to introduce a latent variable which facilitates an easy solution to the problem. We also discuss a novel approach to sampling truncated densities via a “black-box” algorithm, based on the latent variable idea, valid outside of the context of a Gibbs sampler.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Judith Broom
Date Deposited: 06 Nov 2008 20:44
Last Modified: 25 Jun 2014 10:55
Resource URI: http://kar.kent.ac.uk/id/eprint/10553 (The current URI for this page, for reference purposes)
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