BayesPy: Variational Bayesian inference in Python

Jaakko Luttinen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
37 Downloads (Pure)

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.

Original languageEnglish
Article number41
Pages (from-to)1-6
JournalJournal of Machine Learning Research
Volume17
Publication statusPublished - 1 Apr 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Probabilistic programming
  • Python
  • Variational Bayes

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