Online Program Home
My Program

Abstract Details

Activity Number: 21
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #318764
Title: A Fully Bayesian Strategy for High-Dimensional Hierarchical Modeling Using Massively Parallel Computing
Author(s): William Landau* and Jarad Niemi
Companies: Iowa State University and Iowa State University
Keywords: hierarchical model ; statistical genomics ; Markov chain Monte Carlo ; high-dimensional ; high-performance computing ; Bayesian

Markov chain Monte Carlo (MCMC) is the predominant tool used in Bayesian parameter estimation for hierarchical models. When the model expands due to an increasing number of hierarchical levels, number of groups at a particular level, or number of observations in each group, a fully Bayesian analysis via MCMC can easily become computationally intractable. We illustrate how the steps in an MCMC for hierarchical models are predominantly one of two types: conditionally independent draws or low-dimensional draws based on summary statistics of parameters at higher levels of the hierarchy. Parallel computing can increase efficiency by performing embarrassingly parallel computations for conditionally independent draws and calculating the summary statistics using parallel reductions. During the MCMC algorithm, we record running means and means of squared parameter values to allow convergence diagnosis and posterior inference while avoiding the costly memory transfer bottleneck. We demonstrate the effectiveness of the algorithm on a model motivated by next generation sequencing data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association