On Bayesian nonlinear regression with an enzyme example

Abstract

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.