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@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@college-de-france.fr
> 3. http://www.bayesian-programming.org/
> _______________________________________________
> Comp-neuro mailing list
> Comp-neuro@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
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