This manuscript provides step-by-step instructions for implementing Bayesian functional regression models using Stan. Extensive simulations indicate that the inferential performance of the methods is comparable to that of state-of-the-art frequentist approaches. However, Bayesian approaches allow for more flexible modeling and provide an alternative when frequentist methods are not available or may require additional development. Methods and software are illustrated using the accelerometry data from the National Health and Nutrition Examination Survey (NHANES).

翻译：本手稿提供了使用Stan实现贝叶斯函数回归模型的逐步指导。大量模拟实验表明，该方法的推断性能与最先进的频率主义方法相当。然而，贝叶斯方法允许更灵活的建模，并在频率主义方法不可用或需要额外开发时提供替代方案。文中使用美国国家健康与营养调查（NHANES）的加速度计数据对方法与软件进行了演示。