We consider fixed scan Gibbs and block Gibbs samplers for a Bayesian hierarchical random effects model with proper conjugate priors. A drift condition given in Meyn and Tweedie (1993, Chapter 15) is used to show that these Markov chains are geometrically ergodic. Showing that a Gibbs sampler is geom
On the Geometrical Convergence of Gibbs Sampler inRd
โ Scribed by Chii-Ruey Hwang; Shuenn-Jyi Sheu
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 273 KB
- Volume
- 66
- Category
- Article
- ISSN
- 0047-259X
No coin nor oath required. For personal study only.
โฆ Synopsis
The geometrical convergence of the Gibbs sampler for simulating a probability distribution in R d is proved. The distribution has a density which is a bounded perturbation of a log-concave function and satisfies some growth conditions. The analysis is based on a representation of the Gibbs sampler and some powerful results from the theory of Harris recurrent Markov chains.
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