Examples

A curated set of Jupyter notebooks that demonstrate the most common tasks in pgmpy - building models, learning from data, inference, and causal analysis.

Defining Bayesian Networks

• Creating Discrete Bayesian Networks
• Creating Linear Gaussian Bayesian Networks
• How to define TabularCPD and LinearGaussianCPD
• Basic Operations on Bayesian Networks

Causal Discovery / Structure Learning

• Structure Learning in Bayesian Networks
• Learning Tree Structure from Data using the Chow-Liu Algorithm
• Learning Tree-augmented Naive Bayes (TAN) Structure from Data

Parameter Estimation

• Parameter Learning in Discrete Bayesian Networks
• Marginal Learning in Discrete Markov Networks
• Modeling the home & visitor scores

Probabilistic Inference

• Inference in Discrete Bayesian Network
• Monty Hall Problem

Causal Inference

• Causal Inference Examples
• Causal Games

Simulations

• Simulating Data From Bayesian Networks

Extending pgmpy

• Extending pgmpy