pgmpy
dev branch

Getting Started

  • Installation
  • Contributing to pgmpy
  • License

Base Structures

  • Directed Acyclic Graph (DAG)
  • Partial Directed Acyclic Graph (PDAG)

Models

  • Bayesian Network
  • Dynamic Bayesian Network (DBN)
  • Structural Equation Models (SEM)
  • Naive Bayes
  • NoisyOr Model
  • Markov Network
  • Junction Tree
  • Cluster Graph
  • Factor Graph
  • Markov Chain

Parameterization

  • Discrete
  • Continuous
  • Discretizing Methods

Exact Inference

  • Variable Elimination
  • Elimination Ordering
  • Belief Propagation
  • Causal Inference
  • MPLP
  • Dynamic Bayesian Network Inference
  • Model Testing

Approximate Inference

  • Approximate Inference Using Sampling
  • Bayesian Model Sampling
  • Gibbs Sampling

Parameter Estimation

  • Maximum Likelihood Estimator
  • Bayesian Estimator
  • Expectation Maximization (EM)
  • Structural Equation Model Estimators

Structure Learning

  • PC (Constraint-Based Estimator)
  • Conditional Independence Tests for PC algorithm
  • Hill Climb Search
  • Structure Score
  • Tree Search
  • Mmhc Estimator
  • Exhaustive Search

Model Testing

  • Metrics for testing models

Input/Output

  • BIF (Bayesian Interchange Format)
  • UAI
  • XMLBIF
  • PomdpX
  • XMLBeliefNetwork

Example Notebooks

  • 1. Example Using the Earthquake network
  • 2. Monty Hall Problem
  • 3. Creating discrete Bayesian Networks
  • 4. Inference in Discrete Bayesian Network
  • 5. Causal Games
  • 6. Causal Inference Examples
  • 7. Parameter Learning in Discrete Bayesian Networks
  • 8. Structure Learning in Bayesian Networks
  • 9. Learning Tree Structure from Data using the Chow-Liu Algorithm
  • 10. Learning Tree-augmented Naive Bayes (TAN) Structure from Data
  • 11. Normal Bayesian Network (no time variation)
  • 12. Extending pgmpy

Tutorial Notebooks

  • 1. Introduction to Probabilitic Graphical Models
  • 2. Bayesian Network
  • 3. Causal Bayesian Networks
  • 4. Markov Networks
  • 5. Exact Inference in Graphical Models
  • 6. Approximate Inference in Graphical Models
  • 7. Parameterizing with Continuous Variables
  • 8. Sampling In Continuous Graphical Models
  • 9. Reading and Writing from pgmpy file formats
  • 10. Learning Bayesian Networks from Data
  • 11. A Bayesian Network to model the influence of energy consumption on greenhouse gases in Italy
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© Copyright 2021, Ankur Ankan.

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