Mastering Probabilistic Graphical Models with Python

Table of Contents

1. Bayesian Network Fundamentals
2. Markov Network Fundamentals
3. Inference: Asking Questions to Models
4. Approximate Inference Methods: Sampling
5. Model Learning: Parameter Estimation in Bayesian Networks
6. Model Learning: Parameter Estimation in Markov Networks
7. Specialized Models