BayesPy: Variational Bayesian Inference in Python
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2014.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.
4 papers.
- 2023pgmpy: A Python Toolkit for Bayesian Networkscited by 21
- 2022cegpy: Modelling with Chain Event Graphs in Pythoncited by 3
- 2023LINFA: a Python library for variational inference with normalizing flow and annealingcited by 0
- 2025Partially stochastic deep learning with uncertainty quantification for model predictive heating controlcited by 0
- 2025Partially stochastic deep learning with uncertainty quantification for model predictive heating control4 Apr 2025
- 2023LINFA: a Python library for variational inference with normalizing flow and annealing10 Jul 2023
- 2023pgmpy: A Python Toolkit for Bayesian Networks17 Apr 2023
- 2022cegpy: Modelling with Chain Event Graphs in Python21 Nov 2022