Probabilistic Graphical Models

Principles and Applications

Luis Enrique Sucar (Autor)

Buch | Hardcover

253 Seiten

2015 | 2015 ed.
Springer London Ltd (Verlag)
978-1-4471-6698-6 (ISBN)

Infos

This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.

Inhalt

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

Part IV: Relational and Causal Models

Relational Probabilistic Graphical Models

Graphical Causal Models

Produkt-Details

• Reihe/Serie: Advances in Pattern Recognition
• Zusatzinfo: 25 Tables, black and white; 4 Illustrations, color; 113 Illustrations, black and white; XXIV, 253 p. 117 illus., 4 illus. in color.
• Verlagsort: England
• Sprache: englisch
• Maße: 155 x 235 mm
• Gewicht: 696 g
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: bayesian classifiers • Bayesian networks • decision networks • hidden Markov models • Influence Diagrams • Learning Graphical Models • Markov Decision Processes • Markov Random Fields • Probabilistic Graphical Models • probabilistic inference
• ISBN-10: 1-4471-6698-1 / 1447166981
• ISBN-13: 978-1-4471-6698-6 / 9781447166986
• Zustand: Neuware