Definition: Full probability model
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Full probability model
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Metadata: ID: D30 | shortcut: fpm | author: JoramSoch | date: 2020-03-03, 16:16.
Definition: Consider measured data $y$ and some unknown latent parameters $\theta$. The combination of a generative model for $y$ in terms of the parameters $\theta$ and a prior distribution on $\theta$ in terms of hyperparameters $\lambda$ is called a full probability model $m$:
\[\label{eq:fpm} m: \, y \sim \mathcal{D}_1(\theta), \, \theta \sim \mathcal{D}_2(\lambda) \; .\]- Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2014): "Probability and inference"; in: Bayesian Data Analysis, ch. 1, p. 3; URL: http://www.stat.columbia.edu/~gelman/book/.
Metadata: ID: D30 | shortcut: fpm | author: JoramSoch | date: 2020-03-03, 16:16.