Smoothness Priors and Nonlinear Regression
平滑先验与非线性回归
Smoothness priors represent prior information that an unknown function does not change slope quickly and hence that the function describes a simple curve (e.g., Wahba 1978). In this article such priors for the multiple nonlinear regression model are developed in such a way that estimates and “standard errors” can be obtained as a natural and conceptually straightforward extension of linear multiple-regression estimation with the addition of dummy variables and dummy…