Distribution learning

Once the network structure is learned, you can choose how to learn the probability
tables selecting a class in the `weka.classifiers.bayes.net.estimate` package.

The `SimpleEstimator` class produces direct estimates of the conditional probabilities,
that is,

where is the alpha parameter that can be set and is by default. With , we get maximum likelihood estimates.

With the `BMAEstimator`, we get estimates for the conditional probability tables based
on Bayes model averaging of all network structures that are substructures of the
network structure learned [1]. This is achieved by estimating the
conditional probability table of a node given its parents as a weighted
average of all conditional probability tables of given subsets of .
The weight of a distribution with
used is proportional
to the contribution of network structure
to either the
BDe metric or K2 metric depending on the setting of the `useK2Prior` option (**false**
and **true** respectively).