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This question is about a concept in the paper "indentifying independence in bayesian network", page 2 and 3.

In a bayesian network, each node represents as variable and the arrow represent the dependence. The standard queries of the bayesian network is like this: giving a variale a, a Bayesian network D, the value y of a set of variables Y, the task is to compute P(b|y), giving evidence y.

Then we should determining: (1)whether the answer to the query is sensitive to the value of a variable a (2)whether the answer to the query is sensitive to the parameters p_a=P(c|pa(c)) stored at node a.

Here I am confused by (2).

First, I think each node represents a ramdon variable, why the information of p_a=P(c|pa(c)) also stored in the node? what does this mean?

second, consider the conditional independence between variable b and a, why we should treat (1) and (2) differently?

Thank you.

the link of the paper: http://www.cs.technion.ac.il/~dang/journal_papers/geiger1990identifying.pdf

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Can you edit your question to include a link to the paper you're discussing? –  sarnold Jul 19 '12 at 1:15
I have posted the link of paper. –  little_math Jul 19 '12 at 20:47
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