Bayesian Networks
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematically sound and computationally efficient way. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When u...
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| Format: | Online |
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| Langue: | anglais |
| Publié: |
IntechOpen
2021
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| Accès en ligne: | ONIX_20210420_9789535105565_1377 |
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| _version_ | 1869520252806627328 |
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| collection | Directory of Open Access Books |
| description | Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematically sound and computationally efficient way. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. First, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Second, a Bayesian network can be used to learn causal relationships, and hence can be used to gain an understanding about a problem domain and to predict the consequences of intervention. Third, because the model has both causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in a causal form) and data. Fourth, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach to avoid the over fitting of data. |
| format | Online |
| id | doab-20.500.12854ir-66019 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-660192024-04-04T14:41:13Z Bayesian Networks Premchaiswadi, Wichian Probability & statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematically sound and computationally efficient way. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. First, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Second, a Bayesian network can be used to learn causal relationships, and hence can be used to gain an understanding about a problem domain and to predict the consequences of intervention. Third, because the model has both causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in a causal form) and data. Fourth, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach to avoid the over fitting of data. 2021-04-20T15:31:43Z 2021-04-20T15:31:43Z 2012 book ONIX_20210420_9789535105565_1377 9789535105565 9789535149972 https://directory.doabooks.org/handle/20.500.12854/66019 eng image/jpeg n/a https://www.intechopen.com/books https://mts.intechopen.com/storage/books/2155/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/2551 10.5772/2551 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9789535105565 9789535149972 IntechOpen 126 open access |
| spellingShingle | Probability & statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics Bayesian Networks |
| title | Bayesian Networks |
| title_full | Bayesian Networks |
| title_fullStr | Bayesian Networks |
| title_full_unstemmed | Bayesian Networks |
| title_short | Bayesian Networks |
| title_sort | bayesian networks |
| topic | Probability & statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| topic_facet | Probability & statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| url | ONIX_20210420_9789535105565_1377 |