Analysis of Convergence Rates of Some Gibbs Samplers on Continuous State Spaces
نویسنده
چکیده
The problem of sampling from a given distribution on high-dimensional continuous spaces arises in the computational sciences and Bayesian statistics, and a frequentlyused solution is Markov chain Monte Carlo (MCMC); see [13] for many examples. Because MCMC methods produce good samples only after a lengthy mixing period, a long-standing mathematical question is to analyze the mixing times of the MCMC algorithms which are in common use. Although there are many mixing conditions, the most commonly used is called the mixing time, and is based on the total variation distance:
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