{"library":"pyagrum-nightly","title":"pyAgrum-nightly: Probabilistic Graphical Models","description":"pyAgrum-nightly is a Python wrapper for the scientific C++ aGrUM library, providing a high-level interface for Bayesian networks, Markov Networks, Influence Diagrams, and other Probabilistic Graphical Models. As a nightly build, it offers the latest features and bug fixes from the development branch. It is actively maintained with frequent updates reflecting ongoing development.","language":"python","status":"active","last_verified":"Mon May 18","install":{"commands":["pip install pyAgrum-nightly"],"cli":null},"imports":["import pyagrum as gum"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pyagrum as gum\n\n# Create a Bayesian Network\nbn = gum.BayesNet(\"WaterSprinkler\")\n\n# Add variables\nid_c = bn.add(gum.LabelizedVariable(\"c\", \"cloudy ?\", 2))\nid_s = bn.add(gum.LabelizedVariable(\"s\", \"sprinkler ?\", 2))\nid_r = bn.add(gum.LabelizedVariable(\"r\", \"rain ?\", 2))\nid_w = bn.add(gum.LabelizedVariable(\"w\", \"wet grass ?\", 2))\n\n# Add arcs (dependencies)\nbn.addArc(id_c, id_s)\nbn.addArc(id_c, id_r)\nbn.addArc(id_s, id_w)\nbn.addArc(id_r, id_w)\n\n# Define Conditional Probability Tables (CPTs) using dictionaries\nbn.cpt(\"c\").fillWith([0.5, 0.5])\nbn.cpt(\"s\")[{\"c\": 0}] = [0.5, 0.5]\nbn.cpt(\"s\")[{\"c\": 1}] = [0.9, 0.1]\nbn.cpt(\"r\")[{\"c\": 0}] = [0.8, 0.2]\nbn.cpt(\"r\")[{\"c\": 1}] = [0.2, 0.8]\nbn.cpt(\"w\")[{\"s\": 0, \"r\": 0}] = [1, 0]\nbn.cpt(\"w\")[{\"s\": 0, \"r\": 1}] = [0.1, 0.9]\nbn.cpt(\"w\")[{\"s\": 1, \"r\": 0}] = [0.1, 0.9]\nbn.cpt(\"w\")[{\"s\": 1, \"r\": 1}] = [0.01, 0.99]\n\n# Perform inference\nie = gum.LazyPropagation(bn)\nie.setEvidence({\"w\": 1}) # Wet grass is true\nie.makeInference()\n\n# Get posterior probabilities\nposterior_c = ie.posterior(\"c\")\nprint(f\"P(c|w=1): {posterior_c}\")","lang":"python","description":"This quickstart demonstrates how to create a simple Bayesian network (the 'Water Sprinkler' example), define its topology and conditional probability tables, and perform inference with evidence. It highlights the use of `pyagrum` (lowercase) for imports and dictionary-based CPT assignments.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-18","installed_version":"1.15.1.9.dev202409251723794729","pypi_latest":"2.3.2.9.dev202605181778867036","is_stale":true,"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":13.8,"avg_import_s":null,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"pyAgrum-nightly","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"pyAgrum-nightly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":15.2,"import_time_s":null,"mem_mb":null,"disk_size":"370M"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"pyAgrum-nightly","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"pyAgrum-nightly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":14.2,"import_time_s":null,"mem_mb":null,"disk_size":"395M"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"pyAgrum-nightly","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"pyAgrum-nightly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":13.8,"import_time_s":null,"mem_mb":null,"disk_size":"377M"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"pyAgrum-nightly","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"pyAgrum-nightly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":14.1,"import_time_s":null,"mem_mb":null,"disk_size":"375M"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"pyAgrum-nightly","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"pyAgrum-nightly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":11.8,"import_time_s":null,"mem_mb":null,"disk_size":"196M"}]}}