# PIMS-UBC Statistics Constance van Eeden Lecture Series: Jeffrey Rosenthal

## Topic

## Details

Markov chain Monte Carlo (MCMC) algorithms, such as the Metropolis Algorithm and the Gibbs Sampler, are an extremely useful and popular method of approximately sampling from complicated probability distributions. Adaptive MCMC attempts to automatically modify the algorithm while it runs, to improve its performance on the fly. However, such adaptation often destroys the ergodicity properties necessary for the algorithm to be valid. In this talk, we first illustrate MCMC algorithms using simple graphical Java applets. We then discuss adaptive MCMC, and present examples and theorems concerning its ergodicity and efficiency. We close with some recent ideas which make adaptive MCMC more widely applicable in broader contexts.

**Biography**:

## Additional Information

**Time**: 4pm - 5pm, with a pre-talk reception at 3.30 pm

**
Location**: Michael Smith Labs, Room 102

Jeffrey Rosenthal, University of Toronto

**Scientific, Distinguished Lecture**

**April 6, 2017**

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