Learning hybrid Bayesian networks using mixtures of truncated exponentials
In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. 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 struct...
| 主要な著者: | , , |
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| フォーマット: | info:eu-repo/semantics/article |
| 言語: | English |
| 出版事項: |
2017
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| 主題: | |
| オンライン･アクセス: | http://hdl.handle.net/10835/4898 https://doi.org/10.1016/j.ijar.2005.10.004 |
| 要約: | In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. 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 structure of the network is obtained by searching over the space of candidate networks using optimisation methods. The conditional densities are estimated by means of Gaussian kernel densities that afterwards are approximated by MTEs, so that the resulting network is appropriate for using standard algorithms for probabilistic reasoning. The behaviour of the proposed algorithm is tested using a set of real-world and arti cially generated databases. |
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