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Probabilistic Graphical Models: Principles and Applications
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Bibliographic Detail
Publisher
Springer-Verlag New York Inc
Publication date
June 30, 2015
Binding
Hardcover
Book category
Adult Non-Fiction
ISBN-13
9781447166986
ISBN-10
1447166981
Dimensions
1 by 6.50 by 9.50 in.
Weight
1.45 lbs.
Published in
Great Britain
Original list price
$69.99
Amazon.com says people who bought this book also bought:
Discrete Probability Models and Methods: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding (Probability Theory and Stochastic Modelling) | Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python | Deep Learning (Adaptive Computation and Machine Learning series) | The Elements of Statistical Learning | Bayesian Networks in R | Pattern Recognition And Machine Learning | Reinforcement Learning | Probabilistic Graphical Models | Decision Making Under Uncertainty
Discrete Probability Models and Methods: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding (Probability Theory and Stochastic Modelling) | Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python | Deep Learning (Adaptive Computation and Machine Learning series) | The Elements of Statistical Learning | Bayesian Networks in R | Pattern Recognition And Machine Learning | Reinforcement Learning | Probabilistic Graphical Models | Decision Making Under Uncertainty
Summaries and Reviews
Amazon.com description: Product 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.
Editions
Hardcover
The price comparison is for this edition
from Springer-Verlag New York Inc (June 30, 2015)
9781447166986 | details & prices | 6.50 × 9.50 × 1.00 in. | 1.45 lbs | List price $69.99
About: This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.
About: This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.
Paperback
With J. I. Rodale |
Revised edition from Grand Central Pub (November 1, 2001); titled "Synonym Finder"
9780446781978 | details & prices | List price $15.95
This edition also contains Synonym Finder
About: Originally published in 1978 by the founder of Rodale Press, The Synonym Finder continues to be a practical reference tool for every home and office.
This edition also contains Synonym Finder
About: Originally published in 1978 by the founder of Rodale Press, The Synonym Finder continues to be a practical reference tool for every home and office.
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