Text

Probabilistic Graphical Models; Principles and Applications

---

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.

---

Availability

No copy data

Detail Information

Series Title

Advances in Computer Vision and Pattern Recognition

Call Number

519.5 SUC p

Publisher

London, Inggris : Springer International Publishing., 2015

Collation

-

Language

English

ISBN/ISSN

978-1-4471-6699-3

Classification

519.5

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Metode statistik

Specific Detail Info

-

Statement of Responsibility

Luis Enrique Sucar

Other Information

Cataloger

Erwin

Source

https://link.springer.com/10.1007/978-1-4471-6699-3

Validator

-

Digital Object Identifier (DOI)

-

Journal Volume

-

Journal Issue

-

Subtitle

-

Parallel Title

-

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment