Generalized additive models (GAMs) are a commonly used, flexible framework applied to many problems in statistical ecology. GAMs are often considered to be a purely frequentist framework (`generalized linear models with wiggly bits'), however links between frequentist and Bayesian approaches to these models were highlighted early on in the literature. Bayesian thinking underlies many parts of the implementation in the popular R package \texttt{mgcv} as well as in GAM theory more generally. This article aims to highlight useful links (and differences) between Bayesian and frequentist approaches to smoothing, and their practical applications in ecology (with an \texttt{mgcv}-centric viewpoint). Here I give some background for these results then move onto two important topics for quantitative ecologists: term/model selection and uncertainty estimation.

翻译：通用添加模型(GAMS)是一个常用的灵活框架,适用于统计生态的许多问题,GAMS通常被视为纯粹的常态框架(“通用线性模型,带有假发位元”),不过,文献早期就强调了常住者和贝叶斯人对这些模型的做法之间的联系,巴伊西亚人的思维是流行的R包(texttt{mgcv})以及更普遍的GAM理论中许多执行部分的基础,这一条旨在强调巴伊西亚人和常住者对平滑法的有益联系(和差异)及其在生态中的实际应用(以\ textt{mgcv}中心观点为中心)。在这里,我为这些结果提供了一些背景,然后转到两个重要的专题,即术语/模式选择和不确定性估计。