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erdogant authored Oct 6, 2024
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* sampling
* comparing two networks
* loading bif files
* conversion of directed to undirected graphs
* Conversion of directed to undirected graphs


#

### Method overview
Learning a Bayesian network can be split into the underneath problems which are all implemented in this package for both discrete, continous and mixed data sets:
Learning a Bayesian network can be split into the underneath problems which are all implemented in this package for both discrete, continuous and mixed data sets:

* **Structure learning**: Given the data: Estimate a DAG that captures the dependencies between the variables.
* There are multiple manners to perform structure learning.
* Constraintsearch or PC
* Exhaustivesearch
* Hillclimbsearch
* NaiveBayes
* TreeSearch
* Chow-liu
* Tree-augmented Naive Bayes (TAN)
* Direct-LiNGAM (for continuous and hybrid datasets)
* ICA-LiNGAM (for continuous and hybrid datasets)

* **Parameter learning**: Given the data and DAG: Estimate the (conditional) probability distributions of the individual variables.
* **Inference**: Given the learned model: Determine the exact probability values for your queries.
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