Librería Portfolio Librería Portfolio

Búsqueda avanzada

TIENE EN SU CESTA DE LA COMPRA

0 productos

en total 0,00 €

PROBABILISTIC GRAPHICAL MODELS. PRINCIPLES AND APPLICATIONS
Título:
PROBABILISTIC GRAPHICAL MODELS. PRINCIPLES AND APPLICATIONS
Subtítulo:
Autor:
SUCAR, L
Editorial:
SPRINGER VERLAG
Año de edición:
2015
ISBN:
978-1-4471-6698-6
Páginas:
253
59,95 €

 

Sinopsis

Includes exercises, suggestions for research projects, and example applications throughout the book
Presents the main classes of PGMs under a single, unified framework
Covers both the fundamental aspects and some of the latest developments in the field




This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. 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. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.