Probabilistic Graphical Models: Principles and Applications

Part I: Fundamentals

Introduction

Probability Theory

Graph Theory

Part II: Probabilistic Models

Bayesian Classifiers

Hidden Markov Models

Markov Random Fields

Bayesian Networks: Representation and Inference

Bayesian Networks: Learning

Dynamic and Temporal Bayesian Networks

Part III: Decision Models

Decision Graphs

Markov Decision Processes

Partially Observable Markov Decision Processes

Part IV: Relational, Causal and Deep Models

Relational Probabilistic Graphical Models

Graphical Causal Models

Causal Discovery

Deep Learning and Graphical Models

A: A Python Library for Inference and Learning

Glossary

Index