Now that ML Parameter Estimation works well, I’ve turned to Bayesian Parameter Estimation (all for discrete variables).

The Bayesian approach is, in practice, very similar to the ML case. Both involves counting how often each state of the variable obtains in the data, conditional of the parents state. I ...

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At the moment pgmpy supports Maximum Likelihood Estimation (MLE) to estimate the conditional probability tables (CPTs) for the variables of a Bayesian Network, given some data set. In my first PR, I’ll refactor the current MLE parameter estimation code to make it a bit nicer to use. This includes ...

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