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, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.

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. He received the National Science Prize en 2016.

�ber die Autorin bzw. den Autor

Dr. Luis Enrique Sucar is a Senior Research Scientist in the Department of Computing at the National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

Anbieterinformationen

GreatBookPrices.com is your top source for finding new books at the absolute lowest prices, guaranteed ! We offer big discounts - everyday - on millions of titles in virtually any category, from Architecture to Zoology -- and everything in between. Discover great deals and super-savings, on professional books, text book titles, the newest computer guides, or your favorite fiction authors. You'll find it all - at HUGE SAVINGS - at GreatBookPrices. Browse through our complete online product catalog today. Serving customers around the world for years, we help thousands find just the books they're looking for -- at incredibly low, bargain prices.

Zur Homepage des Verk�ufers

Unternehmensdaten des Verk�ufers

Far Corner Europe Limited
19-20 Bourne Court, 19-20 Bourne Court, Woodford Green, IG8 8HD, United Kingdom

Gesch�ftsbedingungen und Versandinformationen

Verkaufsbedingungen

Company Name: GreatBookPricesUK
Legal Entity: Far Corner Europe Limited
Address: 19-20 Bourne Court, Southend Road, Woodford Green Essex, UK IG8 8HD
Registration #: 10691061
Authorized representative: Danielle Hainsey

Versandbedingungen

Our warehouses across the globe are fully operational without substantial delays. We are working hard and continue to overcome the daily challenges presented by COVID-19. There have been reports that delivery carriers are experiencing large delays resulting in longer than normal deliveries to customers. We would like to apologize in advance if your item arrives later than the expected delivery due date

Internal processing of your order will take about 1-2 business days. Please allow an additional 10-20 business days for Royal Mail delivery.

Impressum & Info zum Verk�ufer