Joint UConn/UMass
STATISTICS COLLOQUIUM
Erin M. Conlon, Associate Professor
Department of Mathematics and Statistics
University of Massachusetts
Amherst
Parallel Markov Chain Monte Carlo Methods for Bayesian Analysis of Big Data
ABSTRACT
Recently, new parallel Markov chain Monte Carlo (MCMC) methods have been developed for massive data sets that are too large for traditional statistical analysis. These methods partition big data sets into subsets, and implement parallel Bayesian MCMC computation independently on the subsets. The posterior MCMC samples from the subsets are then joined to approximate the full data posterior distributions. Current strategies for combining the subset samples include averaging, weighted averaging and kernel smoothing approaches. Here, I will discuss our new method for combining subset MCMC samples that directly products the subset densities.
While our method is applicable for both Gaussian and non-Gaussian posteriors, we show in simulation studies that our method outperforms existing methods when the posteriors are non-Gaussian. I will also discuss computational tools we have developed for carrying out parallel MCMC computing in Bayesian analysis of big data.
DATE: Wednesday, April 6, 2016
TIME: 4:00 pm - 5:00 pm
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 pm, AUST 326
Pizza after colloquium, AUST 326
For more information, contact: Tracy Burke at tracy.burke@uconn.edu