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9780262013192 - Probabilistic Graphical Models: Principles and Techniques di Koller, Daphne; Friedman, Nir (33 risultati)
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Probabilistic Graphical Models
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Probabilistic Graphical Models
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Hardcover. Condizione: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority!
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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hardcover. Condizione: Fine. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages.
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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hardcover. Condizione: Very Good. Item is gently used and does not show any significant wear.
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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hardcover. Condizione: Good. 1st Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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Probabilistic Graphical Models : Principles and Techniques
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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Probabilistic Graphical Models
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Probabilistic Graphical Models : Principles and Techniques
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Probabilistic Graphical Models
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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Probabilistic Graphical Models : Principles and Techniques
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 108,56
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PROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND TECHNIQUES
Da: Majestic Books, Hounslow, Regno Unito
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PROBABILISTIC GRAPHICAL MODELS
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Probabilistic Graphical Models : Principles and Techniques
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 112,44
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Aggiungi al carrelloCondizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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Probabilistic Graphical Models : Principles and Techniques
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Condizione: As New. Unread book in perfect condition.
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Probabilistic Graphical Models (Hardcover)
Lingua: Inglese
Editore: MIT Press Ltd, Cambridge, Mass., 2009
ISBN 10: 0262013193 ISBN 13: 9780262013192
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
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Hardcover. Condizione: new. 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 decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material- skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs. A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Da: Ria Christie Collections, Uxbridge, Regno Unito
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EUR 126,28
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PROBABILISTIC GRAPHICAL MODELS
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Probabilistic Graphical Models : Principles and Techniques
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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PROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND TECHNIQUES
Da: Biblios, Frankfurt am main, HESSE, Germania
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EUR 148,17
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EUR 127,03
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Aggiungi al carrelloCondizione: New. Daphne Koller is Professor in the Department of Computer Science at Stanford University.Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University.A general framework for constructing and using probab.
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Edizione InternazionalePROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND TECHNIQUES
Da: UK BOOKS STORE, London, LONDO, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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EUR 133,32
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Aggiungi al carrelloHardcover. Condizione: Sehr gut. Gebraucht - Sehr gut Sg - leichte Beschädigungen oder Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - 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 decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
