Bayesian Generalized Additive Model (Bayesian GAM)
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.
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Avoti
- 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 ↗
Kā citēt šo lapu
ScholarGate. (2026, June 3). Bayesian Generalized Additive Model. ScholarGate. https://scholargate.app/lv/statistics/bayesian-generalized-additive-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Beijesas vispārinātais lineārais modelisStatistika↔ compare
- Neiša jauktā modeļa modelisStatistika↔ compare
- Bayesas daudzkārtējā lineārā regresijaStatistika↔ compare
- Vispārīgais aditīvais modelis (GAM)Mašīnmācīšanās↔ compare
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