I am stuck with an excercise which is similar to this one. The idea is to calculate the probability mass function $p(g)$ from the joint probability that a given bayesian network implies. bayesian network

This graph implies the factorization $$p(a,b,c,d,e,f,g) = p(a)*p(b|a)*p(d|b)*p(f|d)*p(g|f)*p(c|a)*p(e|c)*p(g|e)$$ and i know that I can calculate $p(g)$ by marginalizing out the other variables, i.e. : $$ p(g) = \sum_{a,b,c,d,e,f} p(a)*p(b|a)*p(d|b)*p(f|d)*p(g|f)*p(c|a)*p(e|c)*p(g|e). $$ The excercise is to do the calculation by hand but I don't think the idea is too sum up the 2^64 terms so I'm guessin that there is some way to simplify the problem. All the other examples about calculations in bayesian networks had some singly connected components but I can't find a way to simplify the given distribution. I found out that "every variable that is not an ancestor of a query variable or evidence variable is irrelevant to the query", but as all the other variables are ancestors of g I don't think I can simplify this term by pulling in the summation. So any hint on how to solve this is appreciated, thanks! Edit: I'm given exactly the conditional probabilities that appear in the summation.

  • $\begingroup$ Just a minor correction: could it be $p(d|b)$ instead of $p(d|a)$? $\endgroup$
    – T.E.G.
    Commented Nov 6, 2016 at 21:44
  • $\begingroup$ yes, definitely, sorry for the mistake. $\endgroup$
    – Maximal
    Commented Nov 6, 2016 at 22:12


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