While we could in theory directly parameterize this distribution, for efficiency and simplicity we instead write it in terms of the standard p θ (y | P, H) and introduce a new term to approximate the normalization: Throughout we will assume p(P | H) is a fixed constant (justified by the dataset assumption that, lacking y, P and H are independent and drawn at random).
to approximately maximize this objective we need to estimate p(y | H).