Numpy list members, 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. 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 -- Christopher J. Fonnesbeck Department of Mathematics and Statistics University of Otago, PO Box 56 Dunedin, New Zealand