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Optimising and adapting the Metropolis algorithm

Rosenthal, JS (Toronto)
Wednesday 23 June 2010, 11:10-12.00

Seminar Room 1, Newton Institute


The Metropolis algorithm is a very popular method of approximately sampling from complicated probability distributions, especially those arising in Bayesian inference. A wide variety of proposal distributions are available, and it can be dió cult to choose among them. We will discuss optimal proposals under various circumstances. We will also consider the possibility of having the computer automatically "adapt" the algorithm while it runs, to improve and tune on the fly.


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