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Fully Bayesian computing

Jouni Kerman; Andrew E. Gelman

Title:
Fully Bayesian computing
Author(s):
Kerman, Jouni
Gelman, Andrew E.
Date:
Type:
Articles
Department:
Statistics
Permanent URL:
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 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.
Subject(s):
Computer science
Statistics
Item views:
161
Metadata:
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