I need to learn a Bayesian Network Structure from a dataset. I read the book titled "Learning Bayesian Networks" written Neapolitan and Richard but I have no clear idea.
According to the book from the data i can:
1) Create all the DAG Pattern, where a DAG Pattern is an equivalence class of DAG (in the respect of Markov Equivalence).
2) I can create all multinomial augmented bayesian newtowrk correlated to any of the equivalence class;
3) I use a score function to find the best multinomial augmented bayesian newtowrk;
Now i have not understood how to work this scoring function. In the literature, there is more than one? Can you help me understand precisely how to work the main scoring function?
I have also read that this research is hyper-exponential compared to the number of variables N, is that right? instead, there is some other method more efficient?