BayesPy: Variational Bayesian Inference in Python
This provides a tool for Bayesian researchers and practitioners to more easily apply variational inference, though it is incremental as it builds on existing frameworks.
The authors tackled the challenge of implementing variational Bayesian inference by developing BayesPy, a Python package that simplifies model construction and reduces errors, enabling faster development for users.
BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.