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...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/4891 https://doi.org/10.1016/j.ijar.2011.09.005 |
| _version_ | 1789406582583328768 |
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| author | Nielsen, Jens D. Gámez Martín, José Antonio Salmerón Cerdán, Antonio |
| author_facet | Nielsen, Jens D. Gámez Martín, José Antonio Salmerón Cerdán, Antonio |
| author_sort | Nielsen, Jens D. |
| collection | DSpace |
| description | 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 independence relations that are not representable in the structure of more commonly used graphical models such as Bayesian networks and Markov networks. This sometimes makes operations in PDGs more efficient than in alternative models. PDGs have previously been defined only in the discrete case, assuming a multinomial joint distribution over the variables in the model. We extend PDGs to incorporate continuous variables, by assuming a Conditional Gaussian (CG) joint distribution. We also show how inference can be carried out in an efficient way. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-4891 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2017 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-48912023-04-12T19:38:35Z Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs* Nielsen, Jens D. Gámez Martín, José Antonio Salmerón Cerdán, Antonio Probabilistic decision graphs Conditional Gaussian distribution Hybrid Graphical Models Inference 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 independence relations that are not representable in the structure of more commonly used graphical models such as Bayesian networks and Markov networks. This sometimes makes operations in PDGs more efficient than in alternative models. PDGs have previously been defined only in the discrete case, assuming a multinomial joint distribution over the variables in the model. We extend PDGs to incorporate continuous variables, by assuming a Conditional Gaussian (CG) joint distribution. We also show how inference can be carried out in an efficient way. 2017-07-07T07:16:41Z 2017-07-07T07:16:41Z 2012 info:eu-repo/semantics/article http://hdl.handle.net/10835/4891 https://doi.org/10.1016/j.ijar.2011.09.005 en Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| spellingShingle | Probabilistic decision graphs Conditional Gaussian distribution Hybrid Graphical Models Inference Nielsen, Jens D. Gámez Martín, José Antonio Salmerón Cerdán, Antonio Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs* |
| title | Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs* |
| title_full | Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs* |
| title_fullStr | Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs* |
| title_full_unstemmed | Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs* |
| title_short | Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs* |
| title_sort | modelling and inference with conditional gaussian probabilistic decision graphs* |
| topic | Probabilistic decision graphs Conditional Gaussian distribution Hybrid Graphical Models Inference |
| url | http://hdl.handle.net/10835/4891 https://doi.org/10.1016/j.ijar.2011.09.005 |
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