Smoothness Priors and Nonlinear Regression
本文为多元非线性回归模型开发了平滑先验，通过添加虚拟变量和虚拟观测，将估计和标准误的计算扩展为线性回归的自然延伸，并展示了成本函数估计的例子。
Abstract 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 observations. Relations to spline and polynomial interpolation are described. An illustrative example of cost-function estimation is provided.