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Activity Number: 401
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #312015 View Presentation
Title: Efficient Estimation of the Convergence Rate of the Random-Scan Metropolis Algorithm
Author(s): David Spade*+
Companies: University of Missouri-Kansas City
Keywords: Markov chain Monte Carlo ; mixing time ; drift and minorization ; Bayesian Inference ; Computational Statistics
Abstract:

Many situations, especially in Bayesian statistical inference, call for the use of a Markov chain Monte Carlo (MCMC) method as a way to draw approximate samples from an intractable probability distribution. With the use of any MCMC algorithm comes the question of how long the algorithm must run before it can be used to draw an approximate sample from the target distribution. A common method of answering this question involves verifying that the Markov chain satisfies a drift condition and an associated minorization condition. This is often difficult to do analytically, so as an alternative, it is typical to rely on output-based methods of assessing convergence. The work presented here gives a computational method of estimating a drift condition and a minorization condition specifically for the symmetric random-scan Metropolis (RSM) algorithm. Two examples of the use of the method described in this article are provided, and output-based methods of convergence assessment are presented in each example for comparison with the upper bound on the convergence rate obtained via the simulation-based approach.


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