Learning Bayesian networks with the bnlearn R Package
bnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow...
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| Format: | Journal article |
| Language: | English |
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2010
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| _version_ | 1826276703016583168 |
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| author | Scutari, M |
| author_facet | Scutari, M |
| author_sort | Scutari, M |
| collection | OXFORD |
| description | bnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010). |
| first_indexed | 2024-03-06T23:17:53Z |
| format | Journal article |
| id | oxford-uuid:67bda323-26f4-4cf9-bdba-edee6e1f8a0f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:17:53Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:67bda323-26f4-4cf9-bdba-edee6e1f8a0f2022-03-26T18:40:18ZLearning Bayesian networks with the bnlearn R PackageJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:67bda323-26f4-4cf9-bdba-edee6e1f8a0fEnglishSymplectic Elements at Oxford2010Scutari, Mbnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010). |
| spellingShingle | Scutari, M Learning Bayesian networks with the bnlearn R Package |
| title | Learning Bayesian networks with the bnlearn R Package |
| title_full | Learning Bayesian networks with the bnlearn R Package |
| title_fullStr | Learning Bayesian networks with the bnlearn R Package |
| title_full_unstemmed | Learning Bayesian networks with the bnlearn R Package |
| title_short | Learning Bayesian networks with the bnlearn R Package |
| title_sort | learning bayesian networks with the bnlearn r package |
| work_keys_str_mv | AT scutarim learningbayesiannetworkswiththebnlearnrpackage |