Seminar Details

Seminar Details


Apr 3

3:30 pm

Efficient Estimation of Normalizing Constants from Markov Chain Monte Carlo Draws

Florin Vaida


University of Chicago

The estimation of normalizing constants for a family of distributions is a recurrent theme in computational statistics. After a brief description of a number of applications of interest, including likelihood calculation in a genetic linkage problem, I will proceed to examine two different: bridge sampling (Meng and Wong, 1996) and maximum profile likelihood (inverse logistic regression, Geyer, 1993, and Kong, 1996) with an emphasis on the extension to the case of sampling from more than two distributions and on the equivalence of the two methods.

In the end, I will propose new estimation procedures based on tractable transformations of the underlying distributions of the samples, designed for dealing with multimodal continuous densities.