Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms
Markov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistical computation to explore and estimate features of likelihood surfaces and Bayesian posterior distributions. This paper presents simple conditions which ensure the convergence of two widely used versions of MCMC, the Gibbs sampler and Metropolis-Hastings algorithms.
Volume (Year): 49 (1994)
Issue (Month): 2 (February)
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