Definition: Informative and non-informative prior distribution
Index:
The Book of Statistical Proofs ▷
General Theorems ▷
Bayesian statistics ▷
Prior distributions ▷
Informative vs. non-informative
Sources:
Metadata: ID: D118 | shortcut: prior-inf | author: JoramSoch | date: 2020-12-02, 17:28.
Definition: Let $p(\theta \vert m)$ be a prior distribution for the parameter $\theta$ of a generative model $m$. Then,
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the distribution is called an “informative prior”, if it biases the parameter towards particular values;
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the distribution is called a “weakly informative prior”, if it mildly influences the posterior distribution;
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the distribution is called a “non-informative prior”, if it does not influence the posterior hyperparameters.
- 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.