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I fit a nonlinear function with several parameters using scipy.optimize.minimize to minimize the least-squares error. How do I find the variance of the parameters? I tried using the diagonal of the inverse of the Hessian, but the values are 100 times larger than the expected variances. I saw in this question on Cross Validated that the person rescaled the covariance matrix using the "number of measurements" but I don't know what that means.

I realise this question is basically a duplicate of a previous question, but that answer is not sufficient for me. In particular, the first answer says that the scipy.optimize.least_squares function returns the error on the parameters, which is untrue. The second answer does not address the case of least-squares minimization.

user2132672
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  • If you do not indicate why the answers from the previous post are not enough, the question is duplicated. – eyllanesc Apr 19 '18 at 05:26
  • @eyllanesc I edited the question to explain why the other answers are not enough – user2132672 Apr 21 '18 at 06:30
  • Why do not you tell the author of the answer to his error? – eyllanesc Apr 21 '18 at 06:35
  • @eyllanesc Sorry if this is a stupid question but how do I do that without commenting on his answer? I don't have enough reputation to comment. – user2132672 Apr 21 '18 at 06:42
  • There is no other way, get more reputation, I think that 50 points are required to comment, SO offers different ways to obtain reputation: [What is reputation? How do I earn (and lose) it?](https://stackoverflow.com/help/whats-reputation) – eyllanesc Apr 21 '18 at 06:46

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