Monte Carlo methods for Bayesian analysis of constrained parameter problems
Open Access
- 1 March 1998
- journal article
- Published by Oxford University Press (OUP) in Biometrika
- Vol. 85 (1) , 73-87
- https://doi.org/10.1093/biomet/85.1.73
Abstract
Constraints on the parameters in a Bayesian hierarchical model typically make Bayesian computation and analysis complicated. Posterior densities that contain analytically intractable integrals as normalising constants depending on the hyperparameters often make implementation of Gibbs sampling or the Metropolis algorithms difficult. By using reweighting mixtures (Geyer, 1995), we develop alternative simulation-based methods to determine properties of the desired Bayesian posterior distribution. Necessary theory and two illustrative examples are provided.Keywords
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