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...

Полное описание

Библиографические подробности
Главные авторы:	Nielsen, Jens D., Gámez Martín, José Antonio, Salmerón Cerdán, Antonio
Формат:	info:eu-repo/semantics/article
Язык:	English
Опубликовано:	2017
Предметы:	Probabilistic decision graphs Conditional Gaussian distribution Hybrid Graphical Models Inference
Online-ссылка:	http://hdl.handle.net/10835/4891 https://doi.org/10.1016/j.ijar.2011.09.005

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