Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials
In this paper we introduce a hill-climbing algorithm for structural learning of Bayesian networks from databases with discrete and continuous variables. The process is based on the optimisation of a metric that measures the accuracy of a network penalised by its complexity. The result of the algorit...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/report |
| Language: | English |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/10835/1556 |
| _version_ | 1789406724687396864 |
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| author | Romero, Vanessa Rumí, Rafael Salmerón Cerdán, Antonio |
| author_facet | Romero, Vanessa Rumí, Rafael Salmerón Cerdán, Antonio |
| author_sort | Romero, Vanessa |
| collection | DSpace |
| description | In this paper we introduce a hill-climbing algorithm for structural learning of Bayesian networks from databases with discrete and continuous variables. The process is based on the optimisation of a metric that measures the accuracy of a network penalised by its complexity. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The using artificial and real world data. |
| format | info:eu-repo/semantics/report |
| id | oai:repositorio.ual.es:10835-1556 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-15562023-04-12T19:40:27Z Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials Romero, Vanessa Rumí, Rafael Salmerón Cerdán, Antonio In this paper we introduce a hill-climbing algorithm for structural learning of Bayesian networks from databases with discrete and continuous variables. The process is based on the optimisation of a metric that measures the accuracy of a network penalised by its complexity. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The using artificial and real world data. 2012-05-28T09:50:29Z 2012-05-28T09:50:29Z 2004 info:eu-repo/semantics/report Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM'04), pp. 177-184. http://hdl.handle.net/10835/1556 en info:eu-repo/semantics/openAccess Second European Workshop on Probabilistic Graphical Models (PGM'04) |
| spellingShingle | Romero, Vanessa Rumí, Rafael Salmerón Cerdán, Antonio Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials |
| title | Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials |
| title_full | Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials |
| title_fullStr | Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials |
| title_full_unstemmed | Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials |
| title_short | Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials |
| title_sort | structural learning of bayesian networks with mixtures of truncated exponentials |
| url | http://hdl.handle.net/10835/1556 |
| work_keys_str_mv | AT romerovanessa structurallearningofbayesiannetworkswithmixturesoftruncatedexponentials AT rumirafael structurallearningofbayesiannetworkswithmixturesoftruncatedexponentials AT salmeroncerdanantonio structurallearningofbayesiannetworkswithmixturesoftruncatedexponentials |