Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs*

Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs*

Probabilistic decision graphs (PDGs) are probabilistic graphical models that represent a factorisation of a discrete joint probability distribution using a “decision graph”-like structure over local marginal parameters. The structure of a PDG enables the model to capture some context specific indepe...

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Bibliografische gegevens
Hoofdauteurs: Nielsen, Jens D., Gámez Martín, José Antonio, Salmerón Cerdán, Antonio
Formaat: info:eu-repo/semantics/article
Taal:English
Gepubliceerd in: 2017
Onderwerpen:
Probabilistic decision graphs
Conditional Gaussian distribution
Hybrid Graphical Models
Inference
Online toegang:http://hdl.handle.net/10835/4891
https://doi.org/10.1016/j.ijar.2011.09.005

Internet

http://hdl.handle.net/10835/4891
https://doi.org/10.1016/j.ijar.2011.09.005

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