Definition: Informative and non-informative prior distribution

Index: The Book of Statistical Proofs ▷ General Theorems ▷ Bayesian statistics ▷ Prior distributions ▷ Informative vs. non-informative

Definition: Let $p(\theta \vert m)$ be a prior distribution for the parameter $\theta$ of a generative model $m$. Then,

• the distribution is called an “informative prior”, if it biases the parameter towards particular values;

• the distribution is called a “weakly informative prior”, if it mildly influences the posterior distribution;

• the distribution is called a “non-informative prior”, if it does not influence the posterior hyperparameters.

 

Sources:

• Soch J, Allefeld C, Haynes JD (2016): "How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection"; in: NeuroImage, vol. 141, pp. 469-489, eq. 15, p. 473; URL: https://www.sciencedirect.com/science/article/pii/S1053811916303615; DOI: 10.1016/j.neuroimage.2016.07.047.

Metadata: ID: D118 | shortcut: prior-inf | author: JoramSoch | date: 2020-12-02, 17:28.