Probabilistic Graphical Models

This book provides a comprehensive framework for reasoning and decision-making in complex systems, leveraging probabilistic graphical models.

With a focus on model-based reasoning and explicit uncertainty, this approach allows for more faithful representations of real-world applications.

The text covers various models, including Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions for dynamical systems and relational data.

Key concepts are presented alongside advanced techniques, with practical examples from computer vision, robotics, natural language understanding, and computational biology.

Whether you're a student, researcher, or practitioner, this book will provide you with the tools to tackle complex problems in a principled manner.