We study general over-relaxation Markov chain Monte Carlo samplers for multivariate Gaussian densities. We provide conditions for convergence based on the spectral radius of the transition matrix and on detailed balance. We illustrate these algorithms using an image analysis example.
โฆ LIBER โฆ
Over-relaxation methods and coupled Markov chains for Monte Carlo simulation
โ Scribed by Piero Barone; Giovanni Sebastiani; Julian Stander
- Book ID
- 110319026
- Publisher
- Springer US
- Year
- 2002
- Tongue
- English
- Weight
- 186 KB
- Volume
- 12
- Category
- Article
- ISSN
- 0960-3174
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Bayesian inference is a probabilistic inferential method. In the last two decades, it has become more popular than ever due to affordable computing power and recent advances in Markov chain Monte Carlo (MCMC) methods for approximating high dimensional integrals. Bayesian inference can be traced bac