Introduction to pyAgrum

pyAgrum is a Python wrapper for the C++ aGrUM library. It provides a high-level interface to the C++ part of aGrUM allowing to create, manage and perform efficient computations with Bayesian Networks.

The module is generated using the SWIG interface generator. Custom-written code was added to make the interface more user friendly.

pyAgrum aims to allow to easily use (as well as to prototype new algorithms on) Bayesian network and other graphical models.

pyAgrum contains

• a comprehensive API documentation,
• examples as jupyter notebooks,
• and a website.

pyAgrum library

• Functions from pyAgrum
  • Input/Output for bayesian networks
  • Input for influence diagram
• Graphs manipulation
  • Edges and Arcs
    • Arc
    • Edge
  • Directed Graphs
    • Digraph
    • Directed Acyclic Graph
  • Undirected Graphs
    • UndiGraph
    • Clique Graph
  • Mixed Graph
• Random Variables
  • Common API for Random Discrete Variables
  • Concrete classes for Random Discrete Variables
    • LabelizedVariable
    • DiscretizedVariable
    • RangeVariable
• Potential and Instantiation
  • Instantiation
  • Potential
• Bayesian Network
  • Model
  • Tools for Bayesian networks
    • Generation of database
    • Comparison of Bayesian networks
    • Explanation and analysis
  • Inference
  • Exact Inference
    • Lazy Propagation
    • Shafer Shenoy Inference
    • Variable Elimination
  • Approximated Inference
    • Loopy Belief Propagation
    • Sampling
      • Gibbs Sampling
      • Monte Carlo Sampling
      • Weighted Sampling
      • Importance Sampling
    • Loopy sampling
      • Loopy Gibbs Sampling
      • Loopy Monte Carlo Sampling
      • Loopy Weighted Sampling
      • Loopy Importance Sampling
  • Learning
• Probabilistic Relational Models
• Credal Networks
  • Model
  • Inference
• Influence Diagram
  • Model
  • Inference
• Other functions from aGrUM
  • Listeners
    • LoadListener
    • StructuralListener
    • ApproximationSchemeListener
    • DatabaseGenerationListener
  • Random functions
  • OMP functions
• Exceptions from aGrUM

Causality in pyAgrum

• pyAgrum.causal documentation
  • Causal Model
  • Causal Formula
  • Causal Inference
  • Abstract Syntax Tree for Do-Calculus
  • Exceptions
  • Notebook’s tools for causality

pyAgrum.lib

pyAgrum.lib is a set of python tools.

• Module notebook
• Module bn2graph
• Module dynamic bayesian network
• other pyAgrum.lib modules

Indices and tables

• Index
• Module Index
• Search Page

Documentation tools

• Read me/To do