I have a dataset of 150 samples of 60 binary variables. The pcalg binCItest results are not impressive and i can not find a straightforward way to do this using bnlearn or catnet. I appreciate if you let me know your suggestion, especially if accompanied by a code snippet. Thanks
Article Causal Inference Using Graphical Models with the R Package pcalg
About R package for Bayesian network causal inference of binary data, this PDF is good.
You can try MATLAB, e.g., https://cse.buffalo.edu/courses/cse725/peter/Murphy_2003.pdf
I recommend using the bnlearn package in R. It provides you with the ability to generate a modelnetwork (dotstructure) from a givin dataset where you have already discretized your variables of interest according to the states you are trying to gather conditional probabilties from. Also, it’s user flexible allowing you to work with either the hill climbing algorithm or a bayes algorithm. Note! That with your binary data you will have to label your states with letters and mot numbers for bnlearn to work. Simply ctrl find and replace all your numerical discrete values to correct this. Hope this helps!
Hi guys
Does anybody know the pyhton code for continuous bayesian network ?
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