Probabilistic Graphical Models (eBook)

Principles and Applications

Luis Enrique Sucar (Autor)

eBook Download: PDF

2015 | 2015
XXIV, 253 Seiten
Springer London (Verlag)
978-1-4471-6699-3 (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.Topics and features: presents a unified framework encompassing all of the main classes of PGMs; explores the fundamental aspects of representation, inference and learning for each technique; 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; suggests possible course outlines for instructors in the preface.This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.

Inhalt

Part I: FundamentalsIntroductionProbability TheoryGraph TheoryPart II: Probabilistic ModelsBayesian ClassifiersHidden Markov ModelsMarkov Random FieldsBayesian Networks: Representation and InferenceBayesian Networks: LearningDynamic and Temporal Bayesian NetworksPart III: Decision ModelsDecision GraphsMarkov Decision ProcessesPart IV: Relational and Causal ModelsRelational Probabilistic Graphical ModelsGraphical Causal Models

Produkt-Details

• Erscheint lt. Verlag: 19.6.2015
• Reihe/Serie: Advances in Pattern Recognition
• Verlagsort: London
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• 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: 1-4471-6699-X / 144716699X
• ISBN-13: 978-1-4471-6699-3 / 9781447166993