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Last modified: 11 October 2013

Which fit statistics and optimization methods are available?

A Sherpa fit in Iris can be done using a least-squares statistic, chi-squared (with various methods for estimating the variances used by chi-squared), or with either of two maximum likelihood statistics that are useful when the data have low numbers of counts. The Sherpa fit statistics and optimization methods available in Iris are listed below with brief descriptions; refer to the Statistics and Optimization pages of the Sherpa website for a full and detailed explanation of each.

Fit Optimization Methods

Fit Statistics

The Iris default fitting method and statistic are "neldermead" and "leastsq", respectively, which represent good choices for a robust, quick, initial fit of a relatively simple model to a data set covering potentially many orders of magnitude in flux and/or wavelength.

Last modified: 11 October 2013
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