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Let $\theta$ be the set of parameters for a Bayesian Network model. The model observes some data $D$. I want to then predict the values of some new data $D$. Why is the following true?

$$ p(D'|D) = \int p(D'|\theta)p(\theta|D) d\theta $$

Does it stem from some manipulation of Bayes rule? Or the formula for expected value?

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Assuming the dependency of $D'$ on $D$ is only through $\theta$, then $p(D' | \theta) = p(D' | \theta, D)$.

Now you can see all probabilities on the right hand side are simply conditional on $D$: $$ p(D' | D) = \int p(D' | \theta, D) p(\theta | D) d\theta) $$ This behaves exactly as if the condition on $D$ wasn't there, so for educational purposes, let's just forget about it: $$ p(D') = \int p(D' | \theta) p(\theta) d\theta) = \int p(D', \theta) d\theta $$ This last equation just shows the default way of integrating out one variable from a common distribution. As indicated above, this just works the same way if all distributions are conditional.

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