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