Index: The Book of Statistical ProofsModel Selection ▷ Classical information criteria ▷ Deviance information criterion ▷ Deviance

Definition: Let there be a generative model $m$ describing measured data $y$ using model parameters $\theta$. Then, the deviance of $m$ is a function of $\theta$ which multiplies the log-likelihood function with $-2$:

\[\label{eq:dev} D(\theta) = -2 \log p(y|\theta,m) \; .\]

The deviance function serves the definition of the deviance information criterion.


Metadata: ID: D172 | shortcut: dev | author: JoramSoch | date: 2022-03-01, 07:48.