@article{1433270,
      recid = {1433270},
      author = {Sucar, L. Enrique,},
      title = {Probabilistic graphical models : principles and  applications /},
      pages = {1 online resource :},
      abstract = {This 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, graphical models, and  deep learning, as well as an even greater number of  exercises. 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. Topics and features: Presents a unified  framework encompassing all of the main classes of PGMs  Explores the fundamental aspects of representation,  inference and learning for each technique Examines new  material on partially observable Markov decision processes,  and graphical models Includes a new chapter introducing  deep neural networks and their relation with probabilistic  graphical models Covers multidimensional Bayesian  classifiers, relational graphical models, and causal models  Provides substantial chapter-ending exercises, suggestions  for further reading, and ideas for research or programming  projects Describes classifiers such as Gaussian Naive  Bayes, Circular Chain Classifiers, and Hierarchical  Classifiers with Bayesian Networks Outlines the practical  application of the different techniques Suggests possible  course outlines for instructors This classroom-tested work  is suitable as a textbook for an advanced undergraduate or  a graduate course in probabilistic graphical models for  students of computer science, engineering, and physics.  Professionals wishing to apply probabilistic graphical  models in their own field, or interested in the basis of  these techniques, will also find the book to be an  invaluable reference. Dr. Luis Enrique Sucar is a Senior  Research Scientist at the National Institute for  Astrophysics, Optics and Electronics (INAOE), Puebla,  Mexico.},
      url = {http://library.usi.edu/record/1433270},
      doi = {https://doi.org/10.1007/978-3-030-61943-5},
}