{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/13"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"pyAgrum is a Python library for creating, learning, and performing inference on Bayesian Networks and other Probabilistic Graphical Models, with a C++ backend and high-level Python interface.","skillfed_tags":["bayesian-networks","probabilistic-inference","graphical-models"],"use_cases":["Build and query Bayesian networks to model causal relationships and perform probabilistic inference with hard or soft evidence","Learn network structure and parameters from data using the library's training capabilities","Diagnose faults or predict outcomes in systems where uncertainty and conditional dependencies matter","Visualize probabilistic graphical models and export them to standard formats for sharing or downstream analysis","Integrate probabilistic reasoning into financial, insurance, or risk-assessment applications"],"what_it_does":"pyAgrum wraps a C++ probabilistic graphical model library and exposes it through a Python API for Bayesian Networks and related models. It lets you construct networks by adding nodes and arcs, populate conditional probability tables, save and load models in standard formats (BIF), and run inference algorithms to compute posterior probabilities given evidence.\n\nThe library is designed for researchers and practitioners in AI, statistics, and decision analysis who need to model uncertainty and reason under incomplete information. It depends on numpy for numerical operations, matplotlib for visualization, pydot for graph layout, and scikit-learn for machine learning integration. The package supports modern Python versions (3.10\u20133.14) and runs on macOS, Linux, and Windows.","worth_installing":"Yes, if you need Bayesian Networks or probabilistic graphical models in Python. Active maintenance, no known vulnerabilities, dual licensing (LGPL/MIT) gives flexibility, and broad platform support. Caveat: this is a nightly build (released today), so verify stability for your use case; production users may prefer the stable release track."},"id":"pyagrum-nightly","links":{"html":"https://skillfed.io/packages/pyagrum-nightly","md":"https://skillfed.io/packages/pyagrum-nightly.md","pypi":"https://pypi.org/project/pyagrum-nightly/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"LGPL-3.0-only OR MIT","license_treatment":"unclear","name":"pyAgrum-nightly","python_support":"supports_current","summary":"Bayesian networks and other Probabilistic Graphical Models."},"popularity":{"monthly_downloads":310025,"position":7749,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.0.0.9.dev202608141786444173"}