JSM 2004 - Toronto

Abstract #301521

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Activity Number: 440
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301521
Title: Fully Bayesian Computing
Author(s): Jouni Kerman*+ and Andrew Gelman
Companies: Columbia University and Columbia University
Address: 611 Mathematics Bldg., New York, NY, 10027,
Keywords: Bayesian inference ; statistical computation ; computational methods
Abstract:

A fully Bayesian computing environment calls for the possibility of defining vector and matrix objects that may contain both random and deterministic quantities, and 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 general vector object types that can accommodate any type of object, including new random variable objects that are implicitly represented by simulations. We seek to be able to manipulate 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.

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