Probabilistic graphical models : principles and applications / Luis Enrique Sucar.
2021
Q375
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Probabilistic graphical models : principles and applications / Luis Enrique Sucar.
Edition
Second edition.
ISBN
9783030619435 (electronic bk.)
3030619435 (electronic bk.)
9783030619442 (print)
3030619443
9783030619459 (print)
3030619451
3030619427
9783030619428
3030619435 (electronic bk.)
9783030619442 (print)
3030619443
9783030619459 (print)
3030619451
3030619427
9783030619428
Published
Cham : Springer, [2021]
Language
English
Description
1 online resource : illustrations (some color)
Other Standard Identifiers
10.1007/978-3-030-61943-5 doi
Call Number
Q375
Dewey Decimal Classification
003/.54
Summary
This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, graphical models, and deep learning, as well as an even greater number of exercises. 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 Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors 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. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 26, 2021).
Series
Advances in computer vision and pattern recognition. 2191-6586
Available in Other Form
Print version: 9783030619428
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
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All Resources
Table of Contents
Introduction
Probability Theory
Graph Theory
Bayesian Classifiers
Hidden Markov Models
Markov Random Fields
Bayesian Networks: Representation and Inference
Bayesian Networks: Learning
Dynamic and Temporal Bayesian Networks
Decision Graphs
Markov Decision Processes
Partially Observable Markov Decision Processes
Relational Probabilistic Graphical Models
Graphical Causal Models
Causal Discovery
Deep Learning and Graphical Models
A Python Library for Inference and Learning
Glossary
Index.
Probability Theory
Graph Theory
Bayesian Classifiers
Hidden Markov Models
Markov Random Fields
Bayesian Networks: Representation and Inference
Bayesian Networks: Learning
Dynamic and Temporal Bayesian Networks
Decision Graphs
Markov Decision Processes
Partially Observable Markov Decision Processes
Relational Probabilistic Graphical Models
Graphical Causal Models
Causal Discovery
Deep Learning and Graphical Models
A Python Library for Inference and Learning
Glossary
Index.