Index: The Book of Statistical ProofsGeneral Theorems ▷ Bayesian statistics ▷ Probabilistic modeling ▷ Full probability model

Definition: Consider measured data $y$ and some unknown latent parameters $\theta$. The combination of a generative model for $y$ and a prior distribution on $\theta$ is called a full probability model $m$:

$\label{eq:fpm} m: \, y \sim \mathcal{D}(\theta), \, \theta \sim \mathcal{D}(\lambda) \; .$

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Metadata: ID: D30 | shortcut: fpm | author: JoramSoch | date: 2020-03-03, 16:16.