Abstract:

It is common practice in Markov chain Monte Carlo to update the simulation one variable (or block of variables) at a time, rather than conduct a single full-dimensional update. When it is possible to draw from each full-conditional distribution associated with the target this is just a Gibbs sampler. Often at least one of the Gibbs updates is replaced with a Metropolis–Hastings step, yielding a Conditional Metropolis–Hastings algorithm. Strategies for combining component-wise updates include composition, random sequence and random scans. While these strategies can ease MCMC implementation and produce superior empirical performance compared to full-dimensional updates, the theoretical convergence properties of the associated Markov chains have received limited attention. I will present some recent results on the convergence properties of component-wise MCMC methods, paying particular attention to connections between various algorithms. The connection between the theory and methods for implementing MCMC reliably will be emphasized.

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Topic: Colloquia -- Department of Statistics and Actuarial Science, The University of Iowa
Time: Apr 7, 2022 03:30 PM Central Time (US and Canada)

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Meeting ID: 989 2869 3758