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 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).
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- Learning Bayesian Networks with the bnlearn R Package→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
- Learning Bayesian Networks with the bnlearn R Package→Information theory and statisticsLegacy edge — source detail unavailable
- Learning Bayesian Networks with the bnlearn R Package→Causation, Prediction, and SearchLegacy edge — source detail unavailable
- Learning Bayesian Networks with the bnlearn R Package→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
- Learning Bayesian Networks with the bnlearn R Package→Learning Bayesian Networks: The Combination of Knowledge and Statistical DataLegacy edge — source detail unavailable
- Learning Bayesian Networks with the bnlearn R Package→The max-min hill-climbing Bayesian network structure learning algorithmLegacy edge — source detail unavailable
- Learning Bayesian Networks with the bnlearn R Package→Graphical Models in Applied Multivariate StatisticsLegacy edge — source detail unavailable
- Learning Bayesian Networks with the bnlearn R Package→Learning Bayesian NetworksLegacy edge — source detail unavailable
- Causation, Prediction, and Search→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
- The max-min hill-climbing Bayesian network structure learning algorithm→Causation, Prediction, and SearchLegacy edge — source detail unavailable
- The max-min hill-climbing Bayesian network structure learning algorithm→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
- The max-min hill-climbing Bayesian network structure learning algorithm→Learning Bayesian NetworksLegacy edge — source detail unavailable
- The max-min hill-climbing Bayesian network structure learning algorithm→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
- The max-min hill-climbing Bayesian network structure learning algorithm→Learning Bayesian Networks: The Combination of Knowledge and Statistical DataLegacy edge — source detail unavailable
- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data→Causation, Prediction, and SearchLegacy edge — source detail unavailable
- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
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| Bayesian Modeling and Causal Inference | Computer Science |
| Data Management and Algorithms | Computer Science |
| Data Mining Algorithms and Applications | Computer Science |
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