JSM 2005 Online Program

Abstract #303760

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	256
Type:	Topic Contributed
Date/Time:	Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Statistical Computing
Abstract - #303760
Title:	Fully Bayesian Computing
Author(s):	Jouni Kerman*+ and Andrew Gelman
Companies:	Columbia University and Columbia University
Address:	Department of Statistics, New York, NY, 10027-5904, United States
Keywords:	Bayesian inference ; object-oriented programming ; posterior simulation ; random variable objects
Abstract:	A fully Bayesian computing environment calls for the possibility of defining vector and array objects that may contain both random and deterministic quantities in addition to syntax rules that allow treating these objects much like any variables or numeric arrays. Working within the statistical package R, we introduce a new object-oriented framework based on a new random variable data type implicitly represented by simulations. We seek to be able to manipulate random variables and posterior simulation objects conveniently and transparently and provide a basis for further development of methods and functions that can access these objects directly. We illustrate the use of this new programming environment with several examples of Bayesian computing, including posterior predictive checking and the manipulation of posterior simulations. This new environment is fully Bayesian in that the posterior simulations can be handled directly as random variables.

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