Bayesian Programming (Chapman & Hall)

Discover the innovative approach of Probability as an Alternative to Boolean Logic, a compelling exploration of how probability theory and Bayesian computing offer solutions to real-world problems characterized by incomplete or uncertain information. While traditional logic forms the mathematical foundation of rational reasoning and computing, it is limited to scenarios where data is complete and certain. This book presents a powerful alternative framework designed to handle the complexities of incomplete data, making it highly relevant for applications ranging from financial investments to email filtering.

Bayesian Programming emphasizes the use of probabilistic methods as a practical alternative to Boolean logic, introducing new techniques for constructing probabilistic programs tailored to real-world challenges. Authored by the team responsible for designing and implementing an efficient probabilistic inference engine, the book provides numerous Python examples. These examples are available on a supplementary website, which also features an interpreter that allows readers to experiment with this innovative programming approach firsthand.

The book is structured into several comprehensive parts. The first two sections focus on principles and modeling, requiring only a basic foundation in mathematics. Here, the authors introduce the core concepts of Bayesian programming and discuss best practices for probabilistic modeling. Through numerous simple examples, readers gain insight into applying Bayesian modeling across various fields, fostering a practical understanding of the methodology.

The third part delves into formalism and algorithms, synthesizing existing work on Bayesian inference algorithms. Recognizing the importance of an efficient Bayesian inference engine, this section covers the main probabilistic models, general-purpose algorithms, and learning problems, supported by extensive bibliographic references for those seeking deeper technical details. This foundation is essential for automating probabilistic calculus within Bayesian programs.

In the final section, FAQs and a glossary address common questions and clarify complex topics. The authors compare Bayesian programming with possibility theories, discuss the computational complexity of Bayesian inference, and explore the irreducibility of incompleteness. They also examine the philosophical debate between subjectivist and objectivist epistemologies of probability, providing a well-rounded understanding of the subject.

Finally, the book explores the initial steps toward creating a comprehensive Bayesian computing framework. It highlights the need for new modeling methodologies, inference algorithms, programming languages, and hardware architectures. By focusing on these foundational elements, the authors encourage readers to explore emerging fields such as bio-inspired computing and to contribute to the development of innovative programming languages and hardware solutions, paving the way for future advancements in Bayesian Chapman and Hall/CRC

Series Title: Chapman & Hall/CRC Machine Learning & Pattern Recognition

Title: ABIS_BOOK

Author: Pierre Bessiere

Genre: Automatic Control Engineering

Format: Hardcover

Language: English

Publication Date: 2013

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