東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Probabilistic graphical models = pri...

Sucar, Luis Enrique.

FindBook

Google Book

Amazon

博客來

Probabilistic graphical models = principles and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic graphical models/ by Luis Enrique Sucar.
其他題名:	principles and applications /
作者:	Sucar, Luis Enrique.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xxviii, 355 p. :ill. (some col.), digital ;24 cm.
內容註:	Introduction -- Probability Theory -- Graph Theory -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Decision Graphs -- Markov Decision Processes -- Partially Observable Markov Decision Processes -- Relational Probabilistic Graphical Models -- Graphical Causal Models -- Causal Discovery -- Deep Learning and Graphical Models -- A Python Library for Inference and Learning -- Glossary -- Index.
Contained By:	Springer Nature eBook
標題:	Uncertainty (Information theory) -
電子資源:	https://doi.org/10.1007/978-3-030-61943-5
ISBN:	9783030619435

Probabilistic graphical models = principles and applications /
Sucar, Luis Enrique.

Probabilistic graphical modelsprinciples and applications /[electronic resource] :by Luis Enrique Sucar. - Second edition. - Cham :Springer International Publishing :2021. - xxviii, 355 p. :ill. (some col.), digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

Introduction -- Probability Theory -- Graph Theory -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Decision Graphs -- Markov Decision Processes -- Partially Observable Markov Decision Processes -- Relational Probabilistic Graphical Models -- Graphical Causal Models -- Causal Discovery -- Deep Learning and Graphical Models -- A Python Library for Inference and Learning -- Glossary -- Index.

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, graphical models, and deep learning, as well as an even greater number of exercises. 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.

ISBN: 9783030619435

Standard No.: 10.1007/978-3-030-61943-5doiSubjects--Topical Terms:

587701
Uncertainty (Information theory)

LC Class. No.: Q375

Dewey Class. No.: 003.54

Probabilistic graphical models = principles and applications /
LDR:03857nmm a2200361 a 4500 001 2236769
003 DE-He213
005 20201223085712.0
006 m d
007 cr nn 008maaau
008 211111s2021 sz s 0 eng d
020 $a 9783030619435 $q (electronic bk.)
020 $a 9783030619428 $q (paper)
024 7 $a 10.1007/978-3-030-61943-5 $2 doi
035 $a 978-3-030-61943-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q375
072 7 $a UYAM $2 bicssc
072 7 $a COM077000 $2 bisacsh
072 7 $a UYAM $2 thema
072 7 $a UFM $2 thema
082 0 4 $a 003.54 $2 23
090 $a Q375 $b .S942 2021
100 1 $a Sucar, Luis Enrique. $3 2156364
245 1 0 $a Probabilistic graphical models $h [electronic resource] : $b principles and applications / $c by Luis Enrique Sucar.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xxviii, 355 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a Introduction -- Probability Theory -- Graph Theory -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Decision Graphs -- Markov Decision Processes -- Partially Observable Markov Decision Processes -- Relational Probabilistic Graphical Models -- Graphical Causal Models -- Causal Discovery -- Deep Learning and Graphical Models -- A Python Library for Inference and Learning -- Glossary -- Index.
520 $a 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, graphical models, and deep learning, as well as an even greater number of exercises. 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.
650 0 $a Uncertainty (Information theory) $3 587701
650 0 $a Graphical modeling (Statistics) $3 724571
650 1 4 $a Probability and Statistics in Computer Science. $3 891072
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Electrical Engineering. $3 1001838
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Advances in computer vision and pattern recognition. $3 1567575
856 4 0 $u https://doi.org/10.1007/978-3-030-61943-5
950 $a Computer Science (SpringerNature-11645)