Can you interchange sums and products(in Bishop’s book, pattern recognition and machine learning)?

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According to this stackexchange thread, cannot swap sums and products , you cannot interchange sums and products.

$$ \Sigma\Pi x_{i,j} \ne \Pi\Sigma x_{i,j} $$

However, I found in the book, equations 8.61-8.63 patter recognition and machine learning by Bishop, you can do this. Could someone explain why you can interchange sums and products? thank you very much.

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It seems to be specific to the algorithm you are looking at. I found a slide with a concrete example: https://www.cs.auckland.ac.nz/compsci773s1t/lectures/773-GGpdfs/773GG-BeliefPropagation-handouts.pdf

Basically the idea is similar to the following example:

$$x_1y_1z_1 + x_1y_1z_2+x_1y_2z_1+x_1y_2z_2 + x_2y_1z_1 + x_2y_1z_2+x_2y_2z_1+x_2y_2z_2 = (x_1+x_2)(y_1+y_2)(z_1+z_2)$$ but with much more variables.

Notice how the product of sum requires much less computation than the sum of product and that's the point of the algorithm.