000			02809nam a22002177a 4500
008			220207b2009 |||||||| |||| 00| 0 eng d
020			_a9780262013192
082			_a519.5420 KOL-D
100			_aKoller, Daphne
245			_aProbabilistic graphical models : _bprinciples and techniques / _cDaphne Koller and Nir Friedman
260			_aLondon _bMIT Press _c2009
300			_a1233 p.
365			_aUSD _b125.00.
440			_aAdaptive computation and machine learning.
500			_aA 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 are 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.
650			_aGraphical modeling (Statistics)
650			_aGraphical modeling (Statistics)
650			_aBayesian statistical decision theory -- Graphic methods.
650			_aModèles graphiques (Statistique)
700			_aFriedman, Nir
999			_c71659 _d71659