Bayes Blocks: A Python Toolbox for Variational Bayesian Learning
Antti Honkela, Markus Harva, Tapani Raiko, Harri Valpola and Juha Karhunen
In: NIPS*2006 Workshop on Machine Learning Open Source Software, 2006, Whistler, B.C., Canada.

Abstract

Bayes Blocks [1] is a software library implementing variational Bayesian learning of Bayesian networks with rich possibilities for continuous variables [2]. The underlying inference engine has been implemented in C++ with Python bindings to allow developing new models in Python. Version 1.0 of the software was released in July 2005 under the GNU GPL and the development has since continued incrementally. The package is hosted and available for download at PASCAL Forge at http://forge.pascal-network.org/projects/bblocks/.

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