{"id":32153173,"url":"https://github.com/stecrotti/beliefpropagation.jl","last_synced_at":"2025-10-21T11:05:54.796Z","repository":{"id":258519010,"uuid":"699857343","full_name":"stecrotti/BeliefPropagation.jl","owner":"stecrotti","description":"The Belief Propagation approximation for probability distributions on sparse graphs","archived":false,"fork":false,"pushed_at":"2025-07-10T17:26:57.000Z","size":1343,"stargazers_count":25,"open_issues_count":5,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-07-10T18:41:29.145Z","etag":null,"topics":["belief-propagation","graphical-models","inference","markov-random-fields","stat-mech"],"latest_commit_sha":null,"homepage":"","language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/stecrotti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-10-03T13:33:07.000Z","updated_at":"2025-07-10T16:58:52.000Z","dependencies_parsed_at":"2024-10-19T11:31:47.130Z","dependency_job_id":"4aa00402-f5aa-4723-8880-7b9802e56fd8","html_url":"https://github.com/stecrotti/BeliefPropagation.jl","commit_stats":null,"previous_names":["stecrotti/beliefpropagation.jl"],"tags_count":10,"template":false,"template_full_name":null,"purl":"pkg:github/stecrotti/BeliefPropagation.jl","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stecrotti%2FBeliefPropagation.jl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stecrotti%2FBeliefPropagation.jl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stecrotti%2FBeliefPropagation.jl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stecrotti%2FBeliefPropagation.jl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/stecrotti","download_url":"https://codeload.github.com/stecrotti/BeliefPropagation.jl/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stecrotti%2FBeliefPropagation.jl/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280248572,"owners_count":26297926,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-21T02:00:06.614Z","response_time":58,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["belief-propagation","graphical-models","inference","markov-random-fields","stat-mech"],"created_at":"2025-10-21T11:05:49.721Z","updated_at":"2025-10-21T11:05:54.787Z","avatar_url":"https://github.com/stecrotti.png","language":"Julia","funding_links":[],"categories":[],"sub_categories":[],"readme":"# BeliefPropagation\n[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://stecrotti.github.io/BeliefPropagation.jl/stable/)\n[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://stecrotti.github.io/BeliefPropagation.jl/dev/)\n[![Build Status](https://github.com/stecrotti/BeliefPropagation.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/stecrotti/BeliefPropagation.jl/actions/workflows/CI.yml?query=branch%3Amain)\n[![Coverage](https://codecov.io/gh/stecrotti/BeliefPropagation.jl/graph/badge.svg?token=KjSnA3UPCt)](https://codecov.io/gh/stecrotti/BeliefPropagation.jl)\n[![Aqua](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)\n\nThis package implements a generic version of the [Belief Propagation](https://en.wikipedia.org/wiki/Belief_propagation) (BP) algorithm for the approximation of probability distributions factorized on a graph\n```math\n\\begin{equation}\np(x_1,x_2,\\ldots,x_n) \\propto \\prod_{a\\in F} \\psi_a(\\underline{x}_a) \\prod_{i\\in V} \\phi_i(x_i) \n\\end{equation}\n```\nwhere $F$ is the set of factors, $V$ the set of variables, and $\\underline{x}_a$ is the set of variables involved in factor $a$.\n\n## Installation\n```julia\nimport Pkg; Pkg.add(\"BeliefPropagation\")\n```\n\n## Quickstart\nCheck out the [examples](https://github.com/stecrotti/BeliefPropagation.jl/tree/main/examples) folder.\n\n## Overview\nThe goal of this package is to provide a simple, flexible, and ready-to-use interface to the BP algorithm. It is enough for the user to provide the factor graph (encoded in an adjacency matrix or as a [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl) graph) and the factors, everything else is taken care of.\n\nAt the same time, the idea is that refinements can be made to improve performance on a case-by-case basis. For example, messages are stored as `Vector`s by default, but when working with binary variables, one real number is enough, allowing for considerable speed-ups (see the [Ising](https://github.com/stecrotti/BeliefPropagation.jl/blob/9cbc01d6bbd0266531d6047482b8617bb6eb71ab/src/Models/ising.jl#L56) example).\nAlso, a version of BP for continuous variables such as Gaussian BP can be introduced in the framework, although it is not yet implemented.\n\n## See also\n- [BeliefPropagation.jl](https://github.com/ArtLabBocconi/BeliefPropagation.jl): implements BP for the Ising model and the matching problem.\n- [FactorGraph.jl](https://github.com/mcosovic/FactorGraph.jl): implements Gaussian BP and other message-passing algorithms.\n- [ITensorNetworks.jl](https://github.com/ITensor/ITensorNetworks.jl): implements BP as a technique for approximate tensor network contraction.\n- [ReactiveMP.jl](https://github.com/ReactiveBayes/ReactiveMP.jl): allows to solve Bayesian inference problems using message-passing.\n- [CodingTheory](https://github.com/esabo/CodingTheory/blob/93505402aa1add7d8bb5f06bce24f9fd2bd6a560/src/LDPC/MP_decoders.jl): has a specialized implementation of BP for the coding problem\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstecrotti%2Fbeliefpropagation.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstecrotti%2Fbeliefpropagation.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstecrotti%2Fbeliefpropagation.jl/lists"}