Description: Probabilistic Graphical Models Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Principles and Applications Author(s): Luis Enrique Sucar Format: Paperback Publisher: Springer London Ltd, United Kingdom Imprint: Springer London Ltd ISBN-13: 9781447170549, 978-1447170549 Synopsis 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.
Price: 32.99 GBP
Location: Aldershot
End Time: 2025-01-06T03:15:36.000Z
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Book Title: Probabilistic Graphical Models
Number of Pages: 253 Pages
Language: English
Publication Name: Probabilistic Graphical Models: Principles and Applications
Publisher: Springer London LTD
Publication Year: 2016
Subject: Computer Science, Mathematics
Item Height: 235 mm
Item Weight: 4787 g
Type: Textbook
Author: Luis Enrique Sucar
Subject Area: Electrical Engineering
Series: Advances in Computer Vision and Pattern Recognition
Item Width: 155 mm
Format: Paperback
