# BayesPy: Variational Bayesian Inference in Python

arXiv: https://arxiv.org/abs/1410.0870 · HTML: https://mlsanity.com/paper/1410.0870/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2014.

## Built on by (most cited first, 4 of 4)

- [pgmpy: A Python Toolkit for Bayesian Networks](https://mlsanity.com/paper/2304.08639/) (2023)
- [cegpy: Modelling with Chain Event Graphs in Python](https://mlsanity.com/paper/2211.11366/) (2022)
- [LINFA: a Python library for variational inference with normalizing flow and annealing](https://mlsanity.com/paper/2307.04675/) (2023)
- [Partially stochastic deep learning with uncertainty quantification for model predictive heating control](https://mlsanity.com/paper/2504.03350/) (2025)

## Abstract (arXiv)

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.