Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

The Probabilistic Graphical Models book is a comprehensive guide to constructing and using probabilistic models for decision-making in complex systems.

With a focus on model-based reasoning, this framework allows for interpretable models to be constructed and manipulated by reasoning algorithms, making it an ideal solution for tasks that require the use of available information.

The book covers a range of models, including Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions for dealing with dynamical systems and relational data.

Whether you're looking to improve your decision-making abilities or simply want to understand how probabilistic graphical models work, this book is the perfect resource.