[Comp-neuro] [New Book] Bayesian Programming

Yaroslav Halchenko yoh at psychology.rutgers.edu
Mon Dec 16 18:31:01 CET 2013


I would like to complement this post with announcement of few other
related open projects:

1.  https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

which is not only open and free book which you can contribute to as well
- it uses a completely free and open (unlike the code accompanying
this paper with non-commercial restriction) library: PyMC
(http://pymc-devs.github.io/pymc/).

2. http://ski.clps.brown.edu/hddm_docs/index.html

Hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC)

which has recently published in Frontiers (open access):

Wiecki TV, Sofer I and Frank MJ (2013) HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Front. Neuroinform. 7:14. doi: 10.3389/fninf.2013.00014
http://www.frontiersin.org/Neuroinformatics/10.3389/fninf.2013.00014/abstract

Best regards,
Yaroslav

On Sun, 15 Dec 2013, Pierre Bessière wrote:

>    New Book : Bayesian Programming

>    CRC Press: [1]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 Bessi�re - CNRS
>    *****************************
>    LPPA - College de France

>    11 place Marcelin Berthelot
>    75231 Paris Cedex 05
>    FRANCE

>    Mail: [2]Pierre.Bessiere at College-de-France.fr
>    [3]Http://www.Bayesian-Programming.org
>    Skype: Pierre.Bessiere
>    _______________________________

> References

>    Visible links
>    1. http://www.crcpress.com/product/isbn/9781439880326
>    2. mailto:Pierre.Bessiere at college-de-france.fr
>    3. http://www.bayesian-programming.org/

> _______________________________________________
> Comp-neuro mailing list
> Comp-neuro at neuroinf.org
> http://www.neuroinf.org/mailman/listinfo/comp-neuro


-- 
Yaroslav O. Halchenko, Ph.D.
http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
Senior Research Associate,     Psychological and Brain Sciences Dept.
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
WWW:   http://www.linkedin.com/in/yarik        


More information about the Comp-neuro mailing list