Advances in probabilistic graphical models
In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence; contributions to the area are coming...
Kaydedildi:
| Diğer Yazarlar: | , , |
|---|---|
| Materyal Türü: | Livre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Seri Bilgileri: | Studies in Fuzziness and Soft Computing
214 |
| Online Erişim: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Not: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Advances in Probabilistic Graphical Models, Texte imprimé, 9783540689942 |
İçindekiler:
- Foundations Markov Equivalence in Bayesian Networks A Causal Algebra for Dynamic Flow Networks Graphical and Algebraic Representatives of Conditional Independence Models Bayesian Network Models with Discrete and Continuous Variables Sensitivity Analysis of Probabilistic Networks Inference A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks Decisiveness in Loopy Propagation Lazy Inference in Multiply Sectioned Bayesian Networks Using Linked Junction Forests Learning A Study on the Evolution of Bayesian Network Graph Structures Learning Bayesian Networks with an Approximated MDL Score Learning of Latent Class Models by Splitting and Merging Components Decision Processes An Efficient Exhaustive Anytime Sampling Algorithm for Influence Diagrams Multi-currency Influence Diagrams Parallel Markov Decision Processes Applications Applications of HUGIN to Diagnosis and Control of Autonomous Vehicles Biomedical Applications of Bayesian Networks Learning and Validating Bayesian Network Models of Gene Networks The Role of Background Knowledge in Bayesian Classification