The MIT Press, Date: 2009-07-31. Hardcover. Very Good. 1.9685 in x 9.0551 in x 8.0315 in. 2009. The MIT Press ISBN 0262013193 9780262013192 [US]

UsedAcceptable. The item is fairly worn but still readable. The book may have some cosmetic wear (i.e. creased spine/cover, scratches, curled corners, folded pages, sunburn, stains, water damage, bent, torn, damaged binding, dent). - The dust jacket if present, may be marked, and have considerable heavy wear. – The book might be ex-library copy, and may have the markings and stickers associated from the library - The book may have considerable highlights/notes/underlined pages but the text is legible - Accessories such as CD, codes, toys, may not be included - Safe and Secure Mailer - No Hassle Return ISBN 0262013193 9780262013192 [US]

Interior very clean. Minor shelf wear to exterior. Binding is starting to show some stress (see photo of interior), but it is intact. Old price sticker on back.NOTE TO CUSTOMERS OUTSIDE OF NORTH AMERICA: Unfortunately, a book of this size and weight is not eligible for shipment with Biblio's international shipping program. It is also too large for affordable postal shipment. I am a small seller and do not have any special contracts with international shipping companies. The shipping cost will likely be in excess of $100 (which I will have to request *after* you have placed the order). Please do not place an order if you are unwilling to cover this expense. Unfortunately it is not possible to selectively remove bulky items from international listings. Apologies for the inconvenience. Date: 2009. The Mit Press ISBN 0262013193 9780262013192 [US]

Used - isbn: 0262013193 - isbn13: 9780262013192 MIT Press

MIT Press 2009 Hard cover Fine in fine dust jacket. Great condition! Delivery in 4-6 working days, Supplements like CD, Access Code and Scratch Code etc. may or may not be included. Customer satisfaction is our prime priority. Please feel free to contact us.

The MIT Press, Date: 2009-07-31. Hardcover. Good. Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Tracking 2009. The MIT Press ISBN 0262013193 9780262013192 [US]

ISBN10: 0262013193, ISBN13: 9780262013192, [publisher: MIT Press Ltd, Cambridge, Mass.] Hardcover Hardcover. A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason-to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones- representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and ...
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MIT Press , pp. 1280 . Hardback. New. MIT Press ISBN 0262013193 9780262013192 [US]

ISBN10: 0262013193, ISBN13: 9780262013192, [publisher: MIT Press Ltd, Cambridge, Mass.] Hardcover Hardcover. A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason-to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones- representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and ...