I have two doubt, one about theory and one about practical problem.

First i have not full understand how to work a bayesian network with continuous values. I have learn that i can approximate P(A) (the probability of node A) with a Gaussian Distribution. But i have a dataset, mean and variance of the Gaussian Distribution is the mean and the variance of the dataset ?

And if i have P(A|B,C), with A and B with cotinuous values, how i can represent with a Gaussian Distribution?

The pratical problem is i need to learn a a Bayesian Structur from a continuos values Dataset and i use this toolbox for matlab: http://code.google.com/p/bnt/ (Bayes Net Toolbox for Matlab by Kevin Murphy)

Now how i can use to learn a Bayesian Structur from a Dataset (of continuos values) with this tools?

If i use learn_struct_K2 function i need the order of nodes but where i can get this order? There are other useful functions in this toolbox that you know? (Abaout this problem)

I have two doubt, one about theory and one about practical problem.

First i have not full understand how to work a bayesian network with continuous values. I have learn that i can approximate P(A) (the probability of node A) with a Gaussian Distribution. But i have a dataset, mean and variance of the Gaussian Distribution is the mean and the variance of the dataset ?

And if i have P(A|B,C), with A and B with continuous values, how i can represent with a Gaussian Distribution?

The practical problem is i need to learn a a Bayesian Structure from a continuous values Dataset and i use this toolbox for matlab: http://code.google.com/p/bnt/ (Bayes Net Toolbox for Matlab by Kevin Murphy)

Now how i can use to learn a Bayesian Structure from a Dataset (of continuous values) with this tools?

If i use learn_struct_K2 function i need the order of nodes but where i can get this order? There are other useful functions in this toolbox that you know? (About this problem)