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I have a hierarchical Bayesian Network like this: enter image description here

Here:

$R≡$ log level of poisonous gas (radon) in a house

$B≡$ type of house (With a basement or without)

$C≡$ a county in Minnesota where the house is located (84 of those)

$U≡$ log level of uranium in the soil in each of the counties.

So I have the following structure of my Directed Acyclic Graph (DAG): $$P(C,U,B,R)=P(C)P(U|C)P(B|C)P(R|U,B)$$

This is the table I am given. (a part of it, it containes over 900 results)

enter image description here

Now the question I have to answer is:

"Complete the Bayesian network by specifying all the required probability distributions. The resulting posterior distribution must be such that it is possible to sample from at least one of its full conditional distributions."

Could anyone explain to me what exactly am I being asked here? I am new to Bayesian statistics and don't really know how to proceed. Thanks.

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