Fully Bayesian Computing

25 Pages Posted: 11 Sep 2007

See all articles by Andrew Gelman

Andrew Gelman

Columbia University - Department of Statistics and Department of Political Science

Jouni Kerman

Columbia University - Department of Statistics

Date Written: November 2004

Abstract

A fully Bayesian computing environment calls for the possibility of defining vector and array 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 a new random variable data type that is 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 com-puting, 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.

Keywords: Bayesian inference, object-oriented programming, posterior simulation, random variable objects

Suggested Citation

Gelman, Andrew and Kerman, Jouni, Fully Bayesian Computing (November 2004). Available at SSRN: https://ssrn.com/abstract=1010387 or http://dx.doi.org/10.2139/ssrn.1010387

Andrew Gelman (Contact Author)

Columbia University - Department of Statistics and Department of Political Science ( email )

New York, NY 10027
United States
212-854-4883 (Phone)
212-663-2454 (Fax)

Jouni Kerman

Columbia University - Department of Statistics ( email )

Mail Code 4403
New York, NY 10027
United States

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