Bayesian Generalized additive model
Bayesian Generalized Additive Models extend the frequentist GAM framework by placing prior distributions over the smooth functions and any additional model parameters. This yields full posterior distributions over each smooth effect, enabling principled uncertainty quantification, automatic smoothness selection via hyperpriors, and seamless integration with hierarchical or mixed-effects structures.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). CRC Press. · ISBN 9781498728331
- Bürkner, P.-C. (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1–28. · DOI 10.18637/jss.v080.i01
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