Is it possible to construct a parameter distribution (e.g. Normal or StudentT) from the Estimate and Standard Error of a NonlinearModelFit result? The SE is the square root of the variance but what is the assumption of the underlying parameter distribution and how would the SE feature?
I gather it is possible if one uses Bayesian fitting(e.g. ResourceFunction["BayesianLinearRegression"]) but what about the standard maximum likelihood method that (I assume) NonlinearModelFit uses?