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pyerrors/fits.py
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@ -151,7 +151,6 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`. For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`.
In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix). In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).
This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning). This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
At the moment this option only works for `prior==None` and when no `method` is given.
expected_chisquare : bool expected_chisquare : bool
If True estimates the expected chisquare which is If True estimates the expected chisquare which is
corrected by effects caused by correlated input data (default False). corrected by effects caused by correlated input data (default False).