I would like to better understand the process that Mathematica uses to determine the standard error when fitting with
NonlinearModelFit. I have been reading this post, which explains the code involved in the calculation. My current understanding is that the variable Eh is the precision matrix (a.k.a. concentration matrix) and that taking it's inverse gives the covariance matrix, whose diagonal elements represent the variance of each parameter. What I'm not understanding is how the elements of the precision matrix are related to the derivatives of logL that are calculated in h. Is that relationship generally true? Is it specific to the normal distribution? I have been combing through Google and Wikipedia for the last few hours, not making any headway. Any help or guidance would be greatly appreciated.
edit: JimB, thank you so much for your suggestion. I found this link, which answered my questions. And as for the terminology, I was only using the terms I found by digging through the Wikipedia articles on covariance.