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

Koller, Daphne; Friedman, Nir

ISBN 10: 0262013193 / ISBN 13: 9780262013192

Published by The MIT Press, 2009

About this Item

Condition: Very good.
This book a is a comprehensive, landmark textbook that provides a general framework for constructing and using probabilistic models of complex systems.

Its primary focus is on how to represent and reason about uncertainty in complex, real-world domains like computer vision, robotics, and computational biology.

The book is structured around the three fundamental cornerstones of the probabilistic graphical model (PGM) framework:

Representation: Discusses various models, including Bayesian Networks (directed graphs) and Undirected Markov Networks, as ways to compactly encode joint probability distributions over many variables using conditional independence assumptions.

Inference: Details the algorithms and techniques (both exact and approximate, like belief propagation and sampling methods) for answering probabilistic queries, such as finding the probability of an event given some evidence.

Learning: Covers methods for automatically constructing the models from data, including estimating model parameters and learning the underlying graph structure.

Finally, the book extends the framework to cover advanced topics such as causal reasoning and decision making under uncertainty. It is widely regarded as a definitive reference for students and researchers in artificial intelligence and machine learning.

Seller Inventory # ABE-1760092237570

Bibliographic Details

• Title: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
• Publisher: The MIT Press
• Publication Date: 2009
• Language: English
• ISBN 10: 0262013193
• ISBN 13: 9780262013192
• Binding: Hardcover
• Edition: 1st Edition
• Condition: As New
• Dimensions: 20.777W x 23.419H x 5.207D centimeters 20.777 centimeters width by 23.419 centimeters height by 5.207 centimeters depth
• Weight: 2098 grams
• Seller catalogs:
  • computer science
  • Data science
  • Machine Learning

About this title

Synopsis

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.

About the Author

Daphne Koller is Professor in the Department of Computer Science at Stanford University.

Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University.