BayesPy

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Access rights

openAccess

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2016-04-01

Major/Subject

Mcode

Degree programme

Language

en

Pages

1-6

Series

Journal of Machine Learning Research, Volume 17

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.

Description

Keywords

Probabilistic programming, Python, Variational Bayes

Other note

Citation

Luttinen , J 2016 , ' BayesPy : Variational Bayesian inference in Python ' , Journal of Machine Learning Research , vol. 17 , 41 , pp. 1-6 . < http://www.jmlr.org/papers/volume17/luttinen16a/luttinen16a.pdf >