ENGLISH

Probabilistic Graphical Models: Principles and Applications

By Luis Enrique Sucar

Book information

Publisher
Springer
Year
2015
ISBN
1447166981, 9781447166986, 1447167007, 9781447167006
DOI
10.1007/978-1-4471-6699-3
ASIN
B0101JUD7Y
Language
english
Format
PDF
Filesize
8 MB (8869654 bytes)
Series
Advances in Computer Vision and Pattern Recognition
Edition
1
Pages
277\267
Orientation
portrait
Paginated
yes
Scanned
no
Time added
2015-07-18 18:00:00
Machine Learning; Probabilistic Models; Bayesian N

Description

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.

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