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.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
