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# Interpretation bayesian network graph in SAS EM

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3 weeks ago

Hi,

I try to use the HPBNET node in SAS EM in order to explain a target variable from input variables. By using this node, i would like to know the links between input variables and the effects on my target variable.

I tested several BN structures but only my target variable is a parent in my graph..

Can someone explain to me why my target is always a child ? How is it possible to make predictions if there is no parent variable for my target variable ?

Thanks !

Bruno

I try to use the HPBNET node in SAS EM in order to explain a target variable from input variables. By using this node, i would like to know the links between input variables and the effects on my target variable.

I tested several BN structures but only my target variable is a parent in my graph..

Can someone explain to me why my target is always a child ? How is it possible to make predictions if there is no parent variable for my target variable ?

Thanks !

Bruno

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Posted in reply to Bruno_F

3 weeks ago

It depends on the structure of your network. With a Parent-child or Markov blanket structure, the inputs can be parents of the target. This paper has all the details and equations of how the predictions for a target are obtained from a Bayesian network: https://support.sas.com/resources/papers/proceedings17/SAS0474-2017.pdf

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Posted in reply to WendyCzika

2 weeks ago

Hello WendyCzika,

Thank you for your answer !

I built a Bayesian Network Model in order to explain a binary variable. Is it possible to compute a kind of "contribution" in order to know which input variable is the most contributive in my model to explain my target ?

Also, I picked up the Scoring code from my model but I don't understand the rules on it :

Can someone can explain to me what is compute in this rule (in red) because is not a probability..

else if _I28 = 1

then do;

_target_score_HPBNC1{1}+(**-1.538266175076**);

_target_score_HPBNC1{2}+(**-0.241757117828**);

end;

Best regards,

Bruno