I have search and saw some questions on the matter but without answer (due to the fact that the questions were asked more than 1 year ago, I. hoped something has changed)
I am looking for a library to infer bayesian network from a file of continious variables is there anything simple\out of the box that any one has encountered? I have tried pyAgrum for example but when i run
pyAgrum.BNLearner(numdata).learnDAG()
I get
Exception: [pyAgrum] Wrong type: Counts cannot be performed on continuous variables. Unfortunately the following variable is continuous: V0
Have tried serval libraries but they all seem to work only on discrete variables would love some help in advance.
The main question is what kind of model do you want for your continuous variables.
1- Do you want them to be discretized : you can have a look for instance at http://webia.lip6.fr/~phw/aGrUM/docs/last/notebooks/Discretizer.ipynb.html.
2- Do you want to assume a linear gaussian model : you can have a look for instance at bnlearn (https://haipengu.github.io/Rmd/GBN.html)
3- Do you want to learn more general continuous model : You can have a look at for instance otagrum (http://openturns.github.io/otagrum/master/) which learns copula bayesian network.
4- etc.