Machine learning for BN with hidden variables

Issue #49 closed
Former user created an issue

For building Bayesian network by machine learning, Lea provides the build_bn_from_joint method. This method works fine but can only build up BN made up of observed random variables, by means of a joint probability table. In other words, this method cannot produce a BN with "hidden" variables, that are not observed variables (or even not observable).

It would be interesting that Lea provides a method to learn BN with "hidden" variables based on maximum likelihood estimation. The expectation–maximization (EM) algorithm could be used for this purpose. Alongside, Lea should provide a log-likelihood method to provide a measure of the fitting of a model with observed data.

Comments (20)

  1. Pierre Denis repo owner

    Develop EM algorithm through learn_by_em, em_step and _em_step methods; add Plea, Olea classes for binomial and Poisson distributions (refs #49)

    → <<cset 0c73793ccb8d>>

  2. Pierre Denis repo owner

    Develop EM algorithm through learn_by_em, em_step and _em_step methods; add Plea, Olea classes for binomial and Poisson distributions (refs #49)

    → <<cset 2365f91e4f9a>>

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