000			01455nam a2200181 4500
005			20251016145731.0
008			251008b |||||||| |||| 00| 0 eng d
020			_a9783030619459
082			_a003.54 _bSucP
100			_aSucar, Luis Enrique
245			_aProbabilistic Graphical Models : _bPrinciples and Applications / _cLuis Enrique Sucar
250			_a2nd ed.
260			_bSpringer : _aNature Switzerland , _c©2021.
300			_axxviii,354p.
490			_aAdvances in Computer Vision and Pattern Recognition
520			_aThis fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.
942			_cBK
999			_c6645 _d6645