Approximate MCMC in Theory and Practice
Friday, December 1, 2017 - 3:30pm
Rapid growth in the number of samples in typical datasets and the number of parameters in statistical models poses significant computational challenges. A popular strategy for reducing the computational cost of Markov chain Monte Carlo (MCMC) is to replace the exact Markov kernel with an approximation that is less costly to simulate. We give a number of results on the convergence properties of these approximating Markov chains and the performance of time-averages in approximating expectations of functions with respect to the target measure. The talk is structured around several canonical examples that both motivate the results and illustrate the power and limitations of this approach to scaling up MCMC.
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