BayesPy: Variational Bayesian Inference in Python
Machine Learning
2015-06-08 v3 Computation
Abstract
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.
Keywords
Cite
@article{arxiv.1410.0870,
title = {BayesPy: Variational Bayesian Inference in Python},
author = {Jaakko Luttinen},
journal= {arXiv preprint arXiv:1410.0870},
year = {2015}
}
Comments
Submitted to Journal of Machine Learning Research - Machine Learning Open Source Software