Convergence of Conditional Metropolis-Hastings Samplers, with an Application to Inference for Discretely-Observed Diffusions
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چکیده
We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler. We develop conditions under which this sampler will be geometrically or uniformly ergodic. We apply our results to an algorithm for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.
منابع مشابه
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تاریخ انتشار 2012