The mutual information between two random variables X and Y can be stated formally as follows:

I(X ; Y) = H(X) – H(X | Y)

Where I(X ; Y) is the mutual information for X and Y, H(X) is the entropy for X and H(X | Y) is the conditional entropy for X given Y. The result has the units of bits.

Is the above a realistic representation of the weights along the edge of a bayesian network? Or is a probabilistic representation more suitable? If so, what is the best representation?

How should the edge weights be view from probabilistic perspective in a bayesian network context for directed edges; The probability of the nodes I understand to be posterior or marginal probability, but the edges are slightly more ambiguous.

The mutual information between two random variables X and Y can be stated formally as follows:

I(X ; Y) = H(X) – H(X | Y)

Where I(X ; Y) is the mutual information for X and Y, H(X) is the entropy for X and H(X | Y) is the conditional entropy for X given Y. The result has the units of bits.

Is the above a realistic representation of the weights along the edge of a bayesian network? Or is a probabilistic representation more suitable? If so, what is the best representation?

How should the edge weights be view from probabilistic perspective in a bayesian network context for directed edges; The probability of the nodes I understand to be posterior or marginal probability, but the edges are slightly more ambiguous.

Update 2021/12/08:

How should the edge weights be view from probabilistic perspective in a bayesian network context for directed edges; I expect it is the mutual information, but how is the mathematically represented. The probability of the nodes I understand to be posterior or marginal probability, but the edges are slightly more ambiguous. Please could an expert in this area comment on this?

The mutual information between two random variables X and Y can be stated formally as follows:

I(X ; Y) = H(X) – H(X | Y)

Where I(X ; Y) is the mutual information for X and Y, H(X) is the entropy for X and H(X | Y) is the conditional entropy for X given Y. The result has the units of bits.

Is the above a realistic representation of the weights along the edge of a bayesian network? Or is a probabilistic representation more suitable? If so, what is the best representation?

How should the edge weights be view from probabilistic perspective in a bayesian network context for directed edges; The probability of the nodes I understand to be posterior or marginal probability, but the edges are slightly more ambiguous.