Bayesian views of generalized additive modelling

Links between frequentist and Bayesian approaches to smoothing were highlighted early on in the smoothing literature, and power much of the machinery that underlies the modern generalized additive modelling framework (implemented in software such as the R package $\texttt{mgcv}$), but they tend to be unknown or under appreciated. This article aims to highlight useful links between Bayesian and frequentist approaches to smoothing, and their practical applications (with a somewhat $\texttt{mgcv}$-centric viewpoint).

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