Bayesian Programming

Bayesian Programming

Bayesian Programming

Bayesian Programming (Chapman & Hall/CRC Machine Learning & Pattern Recognition) by Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin and Kamel Mekhnacha
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data.
Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.
Ebook format: PDF
Ebook page: 378
File size: 6.85 MB

$39.00