Bayesian Programming

Bayesian Programming provides a comprehensive introduction to probabilistic programming and Bayesian inference,with a focus on their applications in real-world problems. It covers probability theory, decision-making tools, principles and modeling, formalism and algorithms, and FAQs. The book emphasizes the importance of probability as an alternative to Boolean logic and discusses the first steps toward creating a Bayesian computing framework.

Format: Paperback / softback
Length: 384 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

While logic serves as the mathematical cornerstone of rational reasoning and the fundamental principle of computing, its application is limited to problems where information is both complete and certain. However, numerous real-world challenges, ranging from financial investments to email filtering, exhibit incompleteness or uncertainty. In response to this, probability theory and Bayesian computing offer an alternative framework to handle incomplete and uncertain data.

Decision-Making Tools and Methods for Incomplete and Uncertain Data:

Probability as an Alternative to Boolean Logic:

Bayesian Programming, a comprehensive text, delves into novel approaches for developing probabilistic programs tailored for real-world applications. Authored by the team responsible for designing and implementing an efficient probabilistic inference engine capable of interpreting Bayesian programs, the book provides numerous Python examples, accompanied by a supplementary website offering an interpreter for hands-on experimentation.

Principles and Modeling:

The first two parts of the book offer a groundbreaking methodology for constructing subjective probabilistic models. Requiring only a basic understanding of mathematics, the authors introduce the principles of Bayesian programming and discuss best practices for probabilistic modeling. Numerous practical examples illustrate the application of Bayesian modeling across various fields.

Formalism and Algorithms:

The third part synthesizes existing research on Bayesian inference algorithms, as an efficient Bayesian inference engine is essential for automating the probabilistic calculus in Bayesian programs. Extensive bibliographic references are provided for readers seeking further details on the formalism of Bayesian programming and the fundamental probabilistic concepts involved.

In conclusion, Probability as an Alternative to Boolean Logic and Bayesian Programming provide valuable tools and methodologies for dealing with incomplete and uncertain data in real-world scenarios. By embracing probability theory and Bayesian computing, researchers and practitioners can develop more robust and effective solutions to complex problems, enhancing decision-making processes and achieving better outcomes.

Weight: 710g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032477404