Spring 2009 Thursday, April 16   Speaker: Robert Kohn, Ph.D. Australian School of Business University of New South Wales Title: Adaptive Sampling for Bayesian Inference Abstract: The talk is concerned with the construction of adaptive sampling schemes for Bayesian Inference. Such schemes use previous iterates to tune the proposal distribution automatically and repeatedly. Such schemes have two main advantages over conventional Markov chain Monte Carlo sampling. First, they can be much more efficient than conventional schemes. Second, they are much easier to code because they usually just require problem specific code for the likelihood, with the code for the proposal densities generic. Adaptation needs to be done carefully to ensure convergence to the correct target distribution because the resulting chain is not Markovian. We give conditions for adaptive sampling to work and discuss their application in practice. We introduce several sampling schemes and illustrate the methodology using challenging but realistic models and priors applied to real data examples.

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