New Book : Bayesian Programming
CRC Press: http://www.crcpress.com/product/isbn/9781439880326
Features
• Presents a new modeling methodology and inference algorithms for
Bayesian programming
• Explains how to build efficient Bayesian models
• Addresses controversies, historical notes, epistemological debates,
and tricky technical questions in a dedicated chapter separate from the main
text
• Encourages further research on new programming languages and
specialized hardware for computing large-scale Bayesian inference problems
• Offers an online Python package for running and modifying the Python
program examples in the book
Summary
Probability as an Alternative to Boolean Logic
While 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 Data
Emphasizing 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.
Principles and Modeling
Only requiring a basic foundation in mathematics, the first two parts of the
book present a new methodology for building subjective probabilistic models.
The authors introduce the principles of Bayesian programming and discuss good
practices for probabilistic modeling. Numerous simple examples highlight the
application of Bayesian modeling in different fields.
Formalism and Algorithms
The third part synthesizes existing work on Bayesian inference algorithms since
an efficient Bayesian inference engine is needed to automate the probabilistic
calculus in Bayesian programs. Many bibliographic references are included for
readers who would like more details on the formalism of Bayesian programming,
the main probabilistic models, general purpose algorithms for Bayesian
inference, and learning problems.
FAQ / FAM
Along with a glossary, the fourth part contains answers to frequently asked
questions and frequently argues matters. The authors compare Bayesian
programming and possibility theories, discuss the computational complexity of
Bayesian inference, cover the irreducibility of incompleteness, and address the
subjectivist versus objectivist epistemology of probability.
The First Steps toward a Bayesian Computer
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 encourages readers to
explore emerging areas, such as bio-inspired computing, and develop new
programming languages and hardware architectures.
_______________________________
Dr Pierre Bessière - CNRS
*****************************
LPPA - College de France
11 place Marcelin Berthelot
75231 Paris Cedex 05
FRANCE
Mail: [email protected]
Http://www.Bayesian-Programming.org
Skype: Pierre.Bessiere
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