MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions
This paper introduces MoTBFs, an R package for manipulating mixtures of truncated basis functions. This class of functions allows the representation of joint probability distributions involving discrete and continuous variables simultaneously, and includes mixtures of truncated exponentials and mixt...
| Main Authors: | , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
The R Foundation
2023
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| Online Access: | http://hdl.handle.net/10835/14822 |
| _version_ | 1789406340964155392 |
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| author | Maldonado González, Ana Devaki Salmerón Cerdán, Antonio Pérez Bernabé, Inmaculada Nielsen, Thomas Dyhre |
| author_facet | Maldonado González, Ana Devaki Salmerón Cerdán, Antonio Pérez Bernabé, Inmaculada Nielsen, Thomas Dyhre |
| author_sort | Maldonado González, Ana Devaki |
| collection | DSpace |
| description | This paper introduces MoTBFs, an R package for manipulating mixtures of truncated basis functions. This class of functions allows the representation of joint probability distributions involving discrete and continuous variables simultaneously, and includes mixtures of truncated exponentials and mixtures of polynomials as special cases. The package implements functions for learning the parameters of univariate, multivariate, and conditional distributions, and provides support for parameter learning in Bayesian networks with both discrete and continuous variables. Probabilistic inference using forward sampling is also implemented. Part of the functionality of the
MoTBFs package relies on the bnlearn package, which includes functions for learning the structure of a Bayesian network from a data set. Leveraging this functionality, the MoTBFs package supports learning of MoTBF-based Bayesian networks over hybrid domains. We give a brief introduction to the methodological context and algorithms implemented in the package. An extensive illustrative example is used to describe the package, its functionality, and its usage. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-14822 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2023 |
| publisher | The R Foundation |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-148222023-12-15T13:24:46Z MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions Maldonado González, Ana Devaki Salmerón Cerdán, Antonio Pérez Bernabé, Inmaculada Nielsen, Thomas Dyhre This paper introduces MoTBFs, an R package for manipulating mixtures of truncated basis functions. This class of functions allows the representation of joint probability distributions involving discrete and continuous variables simultaneously, and includes mixtures of truncated exponentials and mixtures of polynomials as special cases. The package implements functions for learning the parameters of univariate, multivariate, and conditional distributions, and provides support for parameter learning in Bayesian networks with both discrete and continuous variables. Probabilistic inference using forward sampling is also implemented. Part of the functionality of the MoTBFs package relies on the bnlearn package, which includes functions for learning the structure of a Bayesian network from a data set. Leveraging this functionality, the MoTBFs package supports learning of MoTBF-based Bayesian networks over hybrid domains. We give a brief introduction to the methodological context and algorithms implemented in the package. An extensive illustrative example is used to describe the package, its functionality, and its usage. 2023-12-15T13:24:46Z 2023-12-15T13:24:46Z 2020-12 info:eu-repo/semantics/article 2073-4859 http://hdl.handle.net/10835/14822 10.32614/RJ-2021-019 en Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess The R Foundation |
| spellingShingle | Maldonado González, Ana Devaki Salmerón Cerdán, Antonio Pérez Bernabé, Inmaculada Nielsen, Thomas Dyhre MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions |
| title | MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions |
| title_full | MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions |
| title_fullStr | MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions |
| title_full_unstemmed | MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions |
| title_short | MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions |
| title_sort | motbfs: an r package for learning hybrid bayesian networks using mixtures of truncated basis functions |
| url | http://hdl.handle.net/10835/14822 |
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