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Statistics Colloquium
January 30 (Wed) 1 p.m.
Alavi Commons Room, 6625 Everett Tower
David Spade
Department of Statistics
The Ohio State University
In biology, it is commonly of interest to investigate the ancestral pattern that gave rise to a currently existing group of individuals, such as genes or species. This ancestral pattern is frequently represented pictorially by a phylogenetic tree. Due to the growing popularity of Bayesian statistical methodology, Markov chain Monte Carlo(MCMC) methods for Bayesian inference of phylogenetic trees have come to the forefront of phylogenetic inference. A common question is how quickly a MCMC algorithm converges to its invariant distribution. The work presented here provides some insight into how to approach the question of convergence for frequently used Markov chains on phylogenetic trees through the use of tools provided by Markov chain theory, instead of through the widely used ad hoc methods of convergence assessment.
All statistics students are expected to attend.