BayesPy

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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 >