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...
Gardado en:
| Outros autores: | , , |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Series: | Studies in Fuzziness and Soft Computing
214 |
| Acceso en liña: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
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 |
| LEADER | 04051nam a22003377a 4500 | ||
|---|---|---|---|
| 001 | 939254 | ||
| 008 | 080410q2000 xx ||| |||| 00| 0 eng d | ||
| 009 | PPN123159326 | ||
| 020 | |a 9783540689966 | ||
| 041 | 0 | |a eng | |
| 082 | |a 519 | ||
| 245 | 0 | 0 | |a Advances in probabilistic graphical models |c edited by Peter Lucas, José A. Gámez, Antonio Salmerón. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Fuzziness and Soft Computing |v 214 |x 1434-9922 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 from computer science, mathematics, statistics and engineering. This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine | ||
| 700 | 1 | |a Lucas, Peter, |d 1955- |4 pbd | |
| 700 | 1 | |a Gámez, José A. |4 pbd | |
| 700 | 1 | |a Salmerón, Antonio. |4 pbd | |
| 776 | 0 | |t Advances in Probabilistic Graphical Models |b Texte imprimé |z 9783540689942 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-540-68996-6 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-GTG7FS8Z-H |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:74788417X |u https://ezproxy.univ-orleans.fr/login?url=https://doi.org/10.1007/978-3-540-68996-6 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:750896884 |u https://ezproxy.insa-cvl.fr/login?qurl=https://doi.org/10.1007/978-3-540-68996-6 |z Accès INSA CVL | |
| 997 | |0 939254 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||