For some Baysian Network situation, you will note that there’s some bodge of values below:

\begin{equation} P(A|M) = \frac{P(M|A)P(A)}{P(M)} \end{equation}

if we are only interested in a function in terms of different values of \(a\), \(P(M)\) is not that interesting. Therefore, we can just calculate \(A\) for all \(a\), and then normalize it to sum to 1:

\begin{equation} P(A|M) \propto P(M|A)P(A) \end{equation}

and then, after calculating each \(P(M|A)P(A)\) , we just ensure that each thing sums to one.