Bayesian Programming

Pierre Bessiere is with CNRS, the French National Centre for Scientific Research. Juan-Manuel Ahuactzin, Kamel Mekhnacha, and Emmanuel Mazer are with Probayes Inc., France.

"Bayesian Programming comprises a methodology, a programming language, and a set of tools for developing and applying … complex models. … The approach is described in great detail, with many worked examples backed up by an online code repository. Unlike other books that tend to focus almost entirely on mathematics, this one gives equal time to conceptual and methodological guidance for the model-builder. It grapples with the knotty problems that arise in practice, some of which do not yet have clear solutions."—From the Foreword by Stuart Russell, University of California, Berkeley"The book has many worked examples backed up by an online code repository. The book provides a contibution on conceptual and methodological guidelines for model-builders. The authors discuss the problem how to build a Bayesian computer. The book has an excellent bibliography."—Nirode C. Mohanty, in Zentralblatt MATH 1281

• Introduction. Bayesian Programming Principles: Basic Concepts. Incompleteness and Uncertainty. Description = Specification + Identification. The Importance of Conditional Independence. Bayesian Program = Description + Question. Bayesian Programming Cookbook: Information Fusion. Bayesian Programming with Coherence Variables. Bayesian Programming Subroutines. Bayesian Programming Conditional Statement. Bayesian Programming Iteration. Bayesian Programming Formalism and Algorithms: Bayesian Programming Formalism. Bayesian Models Revisited. Bayesian Inference Algorithms Revisited. Bayesian Learning Revisited. Frequently Asked Questions and Frequently Argued Matter: Frequently Asked Question and Frequently Argued Matter. Glossary. Index.