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I have a bayesian network like that:

B -> A <- F

And this are the values for A:

P(A=true | B=true, F=false) = 0.01

P(A=true | B=true, F=true) = 0.92

P(A=true | B=false, F=false) = 1.00

P(A=true | B=false, F=true) = 1.00

Now I want to sum out the variable B because I need the values P(A=true | F=true) and P(A=true | F=false). What I thought I have to do is sum this lines where the F values are the same and B is true and false. So line 1 and line 3 on the one side and line 2 and 4 on the other. My result would be:

P(A=true | F=true) = 1.92

P(A=true | F=false) = 1.01

I was quite sure this is right but now I wonder why the sum is greater then 1!? I thought probabilities have to go between 0 and 1? Am I doing something wrong or where is my fault?

  • 1
    You should change the variable $C$ to $F$ in your graphical model in order to be consistent with the rest of your question.2012-05-28
  • 0
    sorry, you're right :)2012-05-29

1 Answers 1