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Hello, I've noticed that the matrix returned by the function "gsl_multifit_covar" is defined as (J^T J)^{-1} Now the problem is that this matrix IS NOT the (approximated) estimated variance-covariance matrix of the asymptotic normal distribution of the estimates. This should indeed be defined as \sigma^2 (J^T J)^{-1} where \sigma^2 = \sum_j res_j / (N-K) res being the residuals, N-K the degrees of freedom. This behavior of gsl_multifit_covar is strange, first because calling a "covariance matrix" what the function returns sound a little bit deceiving and second because it is inconsistent with the covariance matrix returned by the GSL linear routines (which contain the proper factor \sigma^2). Is this a feature or a bug? Best, Giulio.
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