PyMC 2.0

[Chris Fonnesbeck]

Hello everyone,

It gives me great pleasure to be able to announce the long-awaited
release of PyMC 2.0. Platform-specific installers have been uploaded
to the Google Code page (Mac OSX) and the Python Package Index (all
other platforms), along with the new user's guide (http://
pymc.googlecode.com/files/UserGuide2.0.pdf).

PyMC is a python module that implements Bayesian statistical models
and fitting algorithms, including Markov chain Monte Carlo (MCMC). Its
flexibility makes it applicable to a large suite of problems as well as
easily extensible. Along with core sampling functionality, PyMC
includes methods for summarizing output, plotting, goodness-of-fit and
convergence diagnostics.

PyMC 2.0 is a quantum leap from the 1.3 release. It includes a
completely revised object model and syntax, more efficient log-
probability computation, a variety of specialised MCMC algorithms, and
an expanded set of optimised probability distributions. As a result,
models built for previous versions of PyMC will not run under version
2.0.

I would like to particularly thank Anand Patil and David Huard, who
have done most of the work on this version, and to all the users who
have sent questions, comments and bug reports over the past year or
two. Please keep the feedback coming!

Please report any problems with the release to the issues page (http://
code.google.com/p/pymc/issues/list).

Python Package Index: http://pypi.python.org/pypi/pymc/
Google Code: http://pymc.googelcode.com
Mailing List: http://groups.google.com/group/pymc

Happy new year,
Chris Fonnesbeck