lytia85 wrote:When I learn structure of a network with greedy thick thinning method and then with essential graph search with the same data file. How can I choose the best model ? Can I show the score of each model ?
lytia85 wrote:Moreover, if I do supervised analysis, can I export a data file with a new variable composed by the probability of the target learned by the network ?
mark wrote:Please refer to Heckerman's "A Tutorial on Learning With Bayesian Networks" for an explanation of GreedyThickThinning. Essential graph search starts from a graph obtained by applying PC and then continues with a GreedyThickThinning search (and it also does multiple restarts). The learning algorithms automatically select the best networks based on their scores (except PC which doesn't use a score).
It's possible to use any Bayesian network for supervised analysis and it's not necessary to use naive Bayes. GeNIe has target nodes and also a diagnosis module that may be useful in your case.
mark wrote:In a nutshell, the arcs are causal, unless an arc can be reversed without changing the set of conditional independencies that hold for a given graph. The learning is unsupervised.
mark wrote:If the arc can be reversed it means there is a direct correlation between the variables, but the causal relationship cannot be determined
mark wrote: K2 and BDeu are prior distributions over parameters used in the score metric
mark wrote:Do you mean to ask if there is a difference? In general, the runtime depends strongly on the connectivity of the graph you are trying to learn (i.e., the number of conditional dependencies in the data).
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