Learn pyINLA
Priors
Configure priors for the components of your model. Start with the area you want to control, then follow the detailed guide.
Fixed effects
Fixed-Effect Priors
Intercept and coefficients via control['fixed']; defaults, overrides and label-specific priors.
Open fixed-effect priors
Likelihood hyperparameters
Likelihood Hyperpriors
Precision, overdispersion, and shape parameters via control['family']['hyper']; keys and defaults for each family.
Open likelihood hyperpriors
User-defined priors
Custom Priors
Build your own log-density via expression: or table: grids. What works, what doesn't, and worked examples.
Open custom priors