{"product_id":"bayesian-networks-and-decision-graphs-paperback","title":"Bayesian Networks and Decision Graphs - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eThomas Dyhre Nielsen\u003c\/b\u003e (Author), \u003cb\u003eFinn Verner Jensen\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, also introduces Markov decision process. The new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eProbabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis.\u003c\/p\u003e \u003cp\u003eThe book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. \u003c\/p\u003e \u003cp\u003eThe book is a new edition of \u003cem\u003eBayesian Networks and Decision Graphs\u003c\/em\u003e by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cul\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eprovide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003egive practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge.\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003egive several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003epresent a thorough introduction to state-of-the-art solution and analysis algorithms.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003eThe book is intended as a textbook, but it can also be used for self-study and as a reference book.\u003c\/p\u003e \u003cp\u003eFinn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark. \u003c\/p\u003e \u003cp\u003eThomas D. Nielsen is an associate professor at the same department.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 447\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.9 x 9 x 6 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e November 23, 2010\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":54050742206771,"sku":"9781441923943","price":168.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/oQkzg-2QZ09781441923943.webp?v=1788987379","url":"https:\/\/www.vysn.com\/products\/bayesian-networks-and-decision-graphs-paperback","provider":"VYSN","version":"1.0","type":"link"}