{"product_id":"9780387682815","title":"Information Science and Statistics","description":"\u003ch1\u003eInformation Science and Statistics\u003c\/h1\u003e \u003ch2\u003eNielsen, Thomas Dyhre; VERNER JENSEN, FINN\u003c\/h2\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\n\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\n\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\n\u003cp\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cul\u003e\n\u003cp\u003e\n\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\n\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\n\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\n\u003cp\u003e\u0026lt;\n\u003c\/p\u003e\n\u003cp\u003e\n\u003c\/p\u003e\n\u003cli\u003egive several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs.\u003c\/li\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\n\u003c\/p\u003e\n\u003cli\u003epresent a thorough introduction to state-of-the-art solution and analysis algorithms.\u003c\/li\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003c\/p\u003e\n\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 \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2007-06-06\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9780387682815\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-0-387-68282-2\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 447\u003c\/p\u003e ","brand":"Springer New York","offers":[{"title":"Default Title","offer_id":47380284309644,"sku":"9780387682815","price":116.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9780387682815.jpg?v=1775740496","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9780387682815","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}