# eBay/bayesian-belief-networks

Pythonic Bayesian Belief Network Package, supporting creation of and exact inference on Bayesian Belief Networks specified as pure python functions.

Repository: https://github.com/eBay/bayesian-belief-networks
Canonical: https://ross.abutalabs.com/products/bayesian-belief-networks
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2021-04-20T17:10:24+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4780, "days_push": 1961, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1142, forks 256 (observed 2026-08-28T04:03:44.871625+00:00)

## What it is
A Python framework for defining Bayesian Belief Networks and other graphical models as pure Python functions, with support for discrete and Gaussian continuous variables. It provides multiple exact and approximate inference engines including junction tree message passing, sum product, and MCMC sampling.

## Use cases
- build a bayesian belief network in python
- run exact inference on a probabilistic graphical model
- convert a bayesian network to a junction tree or factor graph
- do approximate inference with MCMC sampling on a belief network
- model gaussian bayesian networks with continuous variables
- export a probabilistic graph to graphviz dot format

## When to choose
- you want to define Bayesian networks declaratively in pure Python with minimal dependencies
- you need exact inference (junction tree, sum product) on small-to-medium discrete or Gaussian networks
- you want MCMC sampling as a fallback for approximate inference
- you need GraphViz export for visualizing model structure

## When to avoid
- you need large-scale or scalable inference on massive networks
- you want modern deep-learning-based probabilistic programming (e.g., Pyro, PyMC)
- you need active maintenance or recent releases - the project has seen limited updates since 2021
- you require hybrid networks mixing discrete and continuous variables with full support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, math, data-science
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: bayesian-networks, probabilistic-graphical-models, exact-inference, junction-tree, mcmc, factor-graphs, graphviz-export, algorithms

## Member repositories
- eBay/bayesian-belief-networks (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:44.871625+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:35:05.129454+00:00, confidence not recorded.
  - readme: https://github.com/eBay/bayesian-belief-networks (fetched 2026-08-28T04:03:44.871625+00:00, sha 02f79836de69)
- Data as of 2026-08-30T08:39:29.467469+00:00.