Dear all, I am pleased to announce that BayesPy 0.3 has been released. BayesPy provides tools for variational Bayesian inference. The user can easily constuct conjugate exponential family models from nodes and run approximate posterior inference. BayesPy aims to be efficient and flexible enough for experts but also accessible for casual users. ----------------------------------------------------------------------- This release adds several state-of-the-art VB features. Below is a list of significant new features in this release: * Gradient-based optimization of the nodes by using either the Euclidean or Riemannian/natural gradient. This enables, for instance, the Riemannian conjugate gradient method. * Collapsed variational inference to improve the speed of learning. * Stochastic variational inference to improve scalability. * Pattern search to improve the speed of learning. * Deterministic annealing to improve robustness against initializations. * Gaussian Markov chains can use input signals. More details about the new features can be found here: http://www.bayespy.org/user_guide/advanced.html ------------------------------------------------------ PyPI: https://pypi.python.org/pypi/bayespy/0.3 Git repository: https://github.com/bayespy/bayespy Documentation: http://www.bayespy.org/ Best regards, Jaakko