Reuse, Recycle, Reweigh: Combating Influenza Through Efficient Sequential Bayesian Computation for Massive Data

• Activity Number: 3
• Type: Invited
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Marc Suchard*+
• Companies: University of California at Los Angeles
• Keywords: Influenza ; Bayesian ; Computational statistics ; Disease mapping

Abstract

Massive datasets in the gigabyte and terabyte range yield analyses at the boundary of computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses. In a Bayesian framework, these stratified analyses generate intermediate realizations, often compared using point estimates that fail to account for the variability within and correlation between the distributions these realizations approximate. However, although the initial concession to stratify generally precludes the more sensible analysis using a single joint hierarchical model, we can circumvent this outcome and capitalize on the intermediate realizations by extending the dynamic iterative reweighting MCMC algorithm. In doing so, we reuse the available realizations by reweighting them with importance weights, recycling them into a now tractable joint hierarchical model. We apply this technique to intermediate realizations generated from stratified analyses of 687 influenza A genomes spanning 13 years allowing us to revisit hypotheses regarding the evolutionary history of influenza within a hierarchical statistical framework