| 書目名稱 | Reasoning with Probabilistic and Deterministic Graphical Models | | 副標(biāo)題 | Exact Algorithms, Se | | 編輯 | Rina Dechter | | 視頻video | http://file.papertrans.cn/823/822502/822502.mp4 | | 叢書名稱 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 圖書封面 |  | | 描述 | .Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. Theseproblems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art...This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model‘s graph. We presentinference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in parti | | 出版日期 | Book 2019 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01583-0 | | isbn_softcover | 978-3-031-00455-1 | | isbn_ebook | 978-3-031-01583-0Series ISSN 1939-4608 Series E-ISSN 1939-4616 | | issn_series | 1939-4608 | | copyright | Springer Nature Switzerland AG 2019 |
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