JSM 2011 Online Program

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Abstract Details

Activity Number: 119
Type: Topic Contributed
Date/Time: Monday, August 1, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301285
Title: Issues in Bayesian Datamining
Author(s): John Liechty*+
Companies: Penn State University
Address: 409 BB, Univeristy Park, PA, 16802,
Keywords: Markov chain Monte Carlo ; Data Mining ; Parallel Computing
Abstract:

One advantage of using the Markov chain Monte Carlo (MCMC) algorithm for inference of hierarchical Bayesian models on extreme size data sets is that the sampling algorithm and data can be distributed to multiple nodes of a high performance computer in such a way that draws from full conditional distributions can be accommodated by passing just summaries of relevant parameters, between the different nodes as opposed to passing the data between nodes. These modeling and computation approaches allow for complex inference on extreme data sets in an efficient manner - allowing for schemes that analyze the entire data set as opposed to random subsets. In addition to providing some illustrative examples and results of computer experiments, we discuss the theoretical, convergence properties of the parallelized MCMC algorithm when using an asynchronous scheme for sampling and sharing parameter values across multiple nodes.


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