BayesFusion Support Forum

A few remarks: It seems to me that the amount of data is insufficient to learn the number of parameters you have in the model. Is it possible to obtain more data? Did you include the learned network in the zip file? All the probabilities in the CPTs in DBN_mark_test_17411.xdsl are uniform so I don't...

Can you also post the data file? And the learned network?

This is all possible in GeNIe. CPTs are accessible by double clicking on the nodes and predictions can be made by instantiating evidence and performing inference. This is a manual process, but it can be automated in SMILE.

Learning from soft evidence is not supported in GeNIe and SMILE.

I don't understand what you are trying to do. Could you describe it in more detail, please?

I don't know the time complexity. An exhaustive search is superexponential in the number of variables, and a GreedyThickThinning search may still be bad in the worst case, but in practice it's pretty quick. If you put a limit on the maximum number of parents, it's even better.

Just to clarify, are you asking for the actual running time or the computational complexity?

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).

Yes, these arcs will be part of the network that's outputted. The BDeu weight expresses the strength of a prior belief in the uniformity of the conditional distributions in the network.

If the arc can be reversed it means there is a direct correlation between the variables, but the causal relationship cannot be determined. For example, if you have two discrete variables that are correlated it is not possible to learn from data which way the causal connection goes. K2 and BDeu are p...

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.

The following document has an example dynamic network and data set: http://genie.sis.pitt.edu/wiki/SMILearn_Tutorial_6:_Learning_with_the_EM_Algorithm_%28Dynamic_1%29 The tutorial was written for SMILE, but the network and data set should work in GeNIe too. Each row is one run and dynamic variables ...

Then these settings don't really matter. If there is no data available for a certain parameters in a CPT, then these won't be updated and will keep their original uniform or randomized values.

To answer your questions, can you tell me how much of the data is missing and are there variables that have no data at all?

Yes, you will be overfitting the data. As for your other question, I am not sure, but it's probably what you describe it is.