On Bayesian Nonlinear Regression with an Enzyme Example
将贝叶斯非线性回归模型应用于多组酶动力学数据，以整个回归向量为参数，展示边际后验分布并与传统线性化回归的t分布对比，使用最小信息先验。
A nonlinear regression model is applied to several sets of enzyme kinetics data, treating the entire regression vector as the parameter of interest. The resulting marginal posterior distributions are presented alongside the usual Student's t posterior distributions implicit in the usual linearized regression. The prior distribution used is that which is minimally informative about the regression vector. In the special case of the linear model this procedure leads to the prior density 1/θ, thereby producing the classical inferences.