Hybrid Random Fields : A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an add...
Uloženo v:
| Hlavní autoři: | , |
|---|---|
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2011. |
| Edice: | Intelligent Systems Reference Library
15 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Hybrid Random Fields, Texte imprimé, 9783642203077 • Hybrid Random Fields, Texte imprimé, 9783642268182 • Hybrid Random Fields, Texte imprimé, 9783642203077 • Hybrid Random Fields, Texte imprimé, 9783642203091 • Hybrid Random Fields, Texte imprimé, 9783642268182 • Hybrid Random Fields, Texte imprimé, 9783642203077 • Hybrid Random Fields, Texte imprimé, 9783642203091 |
Obsah:
- Introduction Bayesian Networks Markov Random Fields Introducing Hybrid Random Fields: Discrete-Valued Variables Extending Hybrid Random Fields: Continuous-Valued Variables Applications Probabilistic Graphical Models: Cognitive Science or Cognitive Technology? . Conclusions