Hi! I have just started chapter 13 and am stuck in the first paragraph. In the book, the probabilistic model Pmodel(x) for input data given latent variables (h) is given as,

Pmodel( x ) = E Pmodel ( x | h )

I'm really confused here. How can we obtain a probability distribution by taking expectation of a conditional distribution? Is this related to the law of total probability?