Abstract #301686

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JSM 2003 Abstract #301686
Activity Number: 366
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301686
Title: Sampling for Bayesian Computation with Large Data Sets
Author(s): Zaiying Huang*+ and Andrew Gelman
Companies: Columbia University and Columbia University
Address: Dept. of Statistics, New York, NY, 10027,
Keywords: Bayesian inference ; multilevel model ; MCMC ; Gibbs sampler ; Metropolis algorithm ; cluster sampling
Abstract:

Multilevel models are extremely useful in handling huge hierarchical data sets. However, computation can be a challenge, both in storage and CPU time per iteration of Gibbs sampler or other Markov chain Monte Carlo algorithms. We propose a computational strategy based on sampling the data, computing separate posterior distributions based on each sample, and then combining these to get a consensus posterior inference. With hierarchical data structures, we perform cluster sampling into subsets with the same structures as the original data. This reduces the number of parameters as well as sample size for each separate model fit. We illustrate with examples from climate modeling and newspaper marketing.

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