A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions.Most tasks require a
person or an automated system to reason to reach conclusions based on available information.
The framework of probabilistic graphical models presented in this book provides a general
approach for this task. The approach is model-based allowing interpretable models to be
constructed and then manipulated by reasoning algorithms. These models can also be learned
automatically from data allowing the approach to be used in cases where manually constructing
a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most
real-world applications the book focuses on probabilistic models which make the uncertainty
explicit and provide models that are more faithful to reality. Probabilistic Graphical Models
discusses a variety of models spanning Bayesian networks undirected Markov networks discrete
and continuous models and extensions to deal with dynamical systems and relational data. For
each class of models the text describes the three fundamental cornerstones: representation
inference and learning presenting both basic concepts and advanced techniques. Finally the
book considers the use of the proposed framework for causal reasoning and decision making under
uncertainty. The main text in each chapter provides the detailed technical development of the
key ideas. Most chapters also include boxes with additional material: skill boxes which
describe techniques case study boxes which discuss empirical cases related to the approach
described in the text including applications in computer vision robotics natural language
understanding and computational biology and concept boxes which present significant concepts
drawn from the material in the chapter. Instructors (and readers) can group chapters in various
combinations from core topics to more technically advanced material to suit their particular
needs.
