| 书目名称 | Probabilistic Graphical Models | | 副标题 | Principles and Appli | | 编辑 | Luis Enrique Sucar | | 视频video | | | 概述 | Includes exercises, suggestions for research projects, and example applications throughout the book.Presents the main classes of PGMs under a single, unified framework.Covers both the fundamental aspe | | 丛书名称 | Advances in Computer Vision and Pattern Recognition | | 图书封面 |  | | 描述 | .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.Include | | 出版日期 | Textbook 2021Latest edition | | 关键词 | Bayesian Classifiers; Bayesian Networks; Decision Networks; Hidden Markov Models; Influence Diagrams; Lea | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-030-61943-5 | | isbn_softcover | 978-3-030-61945-9 | | isbn_ebook | 978-3-030-61943-5Series ISSN 2191-6586 Series E-ISSN 2191-6594 | | issn_series | 2191-6586 | | copyright | Springer Nature Switzerland AG 2021 |
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发表于 2025-3-21 17:11:23
