How can I use BIC instead of K2 score?

[EsqYu]

Hi.

I wonder how I can use BIC score instead of K2 score in the add_edges method. I executed the code below, changing scoring function since K2 score seems to be used only in a case that all the variables are discretized. However, it didn't work and the error message says "Hybrid BN does not support continuous data".

from pgmpy.estimators import BicScore hybrid_bn = HybridBN(has_logit=False, use_mixture=True) hybrid_bn.add_edges(data, scoring_function=("Bic",BicScore)) hybrid_bn.fit_parameters(data)

[Anaxagor]

BicScore also only works with discretized data. Currently, structural learning can only be done on discretized data, while parameters learning can be done on initial data without discretization.

[EsqYu]

Thank you for your reply. Then, if I want to apply BAMT on mixed or continuous data without discretization, all I can do is parameters learning? When I only do parameters learning, just execute bn.fit_parameters()?

[Anaxagor]

Yes, parameters learning you can apply without discretization via function bn.fit_parameters().

[EsqYu]

Thank you. I'm just wondering how I can do parameters learning without structure learning. In that case, should I use other algorithm and determine the structure and then use this bn.fit_parameters?

[Anaxagor]

The structure must be specified anyway in a Bayesian network, this can be done by simply passing a list of edges to the method bn.set_structure(edges=structure). Parametric training can then be run as before bn.fit_parameters. There is an example in the documentation https://bamt.readthedocs.io/en/latest/examples/read_structure_param_learning.html