Probabilistic Graphical Models Principles and Techniques Adaptive Computation and Machine Learning series Daphne Koller Nir Friedman 8601401113034 Books Lire en ligne FBR

Probabilistic Graphical Models Principles and Techniques Adaptive Computation and Machine Learning series Daphne Koller Nir Friedman 8601401113034 Books Livres gratuits en ligne à lire Probabilistic%20Graphical%20Models%20Principles%20and%20Techniques%20Adaptive%20Computation%20and%20Machine%20Learning%20series%20Daphne%20Koller%20Nir%20Friedman%208601401113034%20Books

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The Magician and the Cinema Erik Barnouw 9780195029185 Books lis HWL

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.

Daphne Koller, Nir Friedman,Probabilistic Graphical Models Principles and Techniques (Adaptive Computation and Machine Learning series),The MIT Press,0262013193,Machine Theory,Bayesian statistical decision theory - Graphic methods,Bayesian statistical decision theory;Graphic methods.,Graphical modeling (Statistics),Applied,Bayesian statistical decision theory,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,COMPUTERS / Machine Theory,Computer Applications,Computer Books General,Computer Science/Machine Learning Neural Networks,Computer/General,Computers,Computers/Computer Vision Pattern Recognition,Computers/Intelligence (AI) Semantics,Computers/Natural Language Processing,Graphic methods,Intelligence (AI) Semantics,MATHEMATICS / Probability Statistics / General,Machine learning,Mathematics,Mathematics/Applied,Non-Fiction,Probability Statistics - General,Scholarly/Graduate,Technology Engineering/Robotics,Textbooks (Various Levels),UNIVERSITY PRESS,United States,Applied,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,COMPUTERS / Machine Theory,Computers/Computer Vision Pattern Recognition,Computers/Intelligence (AI) Semantics,Computers/Natural Language Processing,Intelligence (AI) Semantics,MATHEMATICS / Probability Statistics / General,Mathematics/Applied,Probability Statistics - General,Technology Engineering/Robotics,Mathematics,Bayesian statistical decision theory,Graphic methods,Computers,Computer Books General,Machine learning

Probabilistic Graphical Models Principles and Techniques Adaptive Computation and Machine Learning series Daphne Koller Nir Friedman 8601401113034 Books Reviews :

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.

Daphne Koller, Nir Friedman,Probabilistic Graphical Models Principles and Techniques (Adaptive Computation and Machine Learning series),The MIT Press,0262013193,Machine Theory,Bayesian statistical decision theory - Graphic methods,Bayesian statistical decision theory;Graphic methods.,Graphical modeling (Statistics),Applied,Bayesian statistical decision theory,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,COMPUTERS / Machine Theory,Computer Applications,Computer Books General,Computer Science/Machine Learning Neural Networks,Computer/General,Computers,Computers/Computer Vision Pattern Recognition,Computers/Intelligence (AI) Semantics,Computers/Natural Language Processing,Graphic methods,Intelligence (AI) Semantics,MATHEMATICS / Probability Statistics / General,Machine learning,Mathematics,Mathematics/Applied,Non-Fiction,Probability Statistics - General,Scholarly/Graduate,Technology Engineering/Robotics,Textbooks (Various Levels),UNIVERSITY PRESS,United States,Applied,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,COMPUTERS / Machine Theory,Computers/Computer Vision Pattern Recognition,Computers/Intelligence (AI) Semantics,Computers/Natural Language Processing,Intelligence (AI) Semantics,MATHEMATICS / Probability Statistics / General,Mathematics/Applied,Probability Statistics - General,Technology Engineering/Robotics,Mathematics,Bayesian statistical decision theory,Graphic methods,Computers,Computer Books General,Machine learning

Probabilistic Graphical Models Principles and Techniques (Adaptive Computation and Machine Learning series) [Daphne Koller, Nir Friedman] on . PBA general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions./B/PPMost tasks require a person or an automated system to reason―to reach conclusions based on available information. The framework of probabilistic graphical models

 

Product details Series Adaptive Computation and Machine Learning series Hardcover 1231 pages Publisher The MIT Press; 1 edition (July 31, 2009) Language English ISBN-10 0262013193

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