Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization
Hybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables. Among inference methods for HBNs, we focus on dynamic discretization (DD) that converts HBN to discrete BN for inference. Complexity of BN inference is exponential on treewidth, which extends to DD for HBNs....
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2022-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/130561 |
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| _version_ | 1849763353648431104 |
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| author | Yang Xiang Hanwen Zheng |
| author_facet | Yang Xiang Hanwen Zheng |
| author_sort | Yang Xiang |
| collection | DOAJ |
| description | Hybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables.
Among inference methods for HBNs, we focus on dynamic discretization (DD)
that converts HBN to discrete BN for inference.
Complexity of BN inference is exponential on treewidth, which extends to DD for HBNs.
We presents a novel framework where HBN is transformed into NAT-modeled BN
(NAT: Non-impeding noisy-AND Tree) for tractable inference.
A case-study under the framework is presented on sum of continuous variables.
We report significant efficiency gain of approximate inference by NAT-modeled DD
over alternative methods. |
| format | Article |
| id | doaj-art-8ece1e15f45c4a96ac09811237a832e0 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-8ece1e15f45c4a96ac09811237a832e02025-08-20T03:05:26ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622022-05-013510.32473/flairs.v35i.13056166760Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic DiscretizationYang Xiang0Hanwen ZhengUniversity of GuelphHybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables. Among inference methods for HBNs, we focus on dynamic discretization (DD) that converts HBN to discrete BN for inference. Complexity of BN inference is exponential on treewidth, which extends to DD for HBNs. We presents a novel framework where HBN is transformed into NAT-modeled BN (NAT: Non-impeding noisy-AND Tree) for tractable inference. A case-study under the framework is presented on sum of continuous variables. We report significant efficiency gain of approximate inference by NAT-modeled DD over alternative methods.https://journals.flvc.org/FLAIRS/article/view/130561bayesian netscausal independence modelsprobabilistic inferencedynamic discretization |
| spellingShingle | Yang Xiang Hanwen Zheng Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization Proceedings of the International Florida Artificial Intelligence Research Society Conference bayesian nets causal independence models probabilistic inference dynamic discretization |
| title | Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization |
| title_full | Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization |
| title_fullStr | Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization |
| title_full_unstemmed | Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization |
| title_short | Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization |
| title_sort | tractable inference for hybrid bayesian networks with nat modeled dynamic discretization |
| topic | bayesian nets causal independence models probabilistic inference dynamic discretization |
| url | https://journals.flvc.org/FLAIRS/article/view/130561 |
| work_keys_str_mv | AT yangxiang tractableinferenceforhybridbayesiannetworkswithnatmodeleddynamicdiscretization AT hanwenzheng tractableinferenceforhybridbayesiannetworkswithnatmodeleddynamicdiscretization |