Pierre Bessiere, Emmanuel Mazer, Juan Ahuactzin, Kamel Mekhnacha

Bayesian Programming

• Gebundenes Buch

Produktbeschreibung

A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It emphasizes probability as an alternative to Boolean logic, covering new methods to build probabilistic programs for real-world applications. The book encourages readers to explore emerging areas and develop new programming languages and hardware architectures. A Python package is available on a supplementary website.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 380
• Erscheinungstermin: 20. Dezember 2013
• Englisch
• Abmessung: 239mm x 157mm x 23mm
• Gewicht: 635g
• ISBN-13: 9781439880326
• ISBN-10: 1439880328
• Artikelnr.: 34761652

Autorenporträt

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.

Inhaltsangabe

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.