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The term parameter space as used in data-fitting (see for example "Data Reduction and Error Analysis for the Physical Sciences" by Bevington and Robinson), refers to the hypothetical space where a "location" is defined by the values of all optimizable parameters. For example, if we fit data using a function which has 10 optimizable parameters, each of these parameters is seen as a dimension and the parameter space in this case is 10-dimensional. Every "location" then corresponds to a χ² (chi-squared) value indicating the goodness-of-fit, hence we have a "field" in our 10-dimensional space. Following this "field" downwards leads us to the "location" in parameter space with the lowest χ², i.e. the optimum parameter values.
Alternatively, χ² can be thought of as an additional dimension. In this case, if we're optimizing 2 variables, variable space is still 2-dimensional, but the addition of χ² as a third dimension results in 3-dimensional "goodness-of-fit" landscapes where the best fit is represented by the lowest point in 3D space.
Examples
For complex quadratic mapping the parameter space is parameter plane (c-plane), in which points (complex numbers) are parameters of a complex quadratic function. In the parameter plane there is a Mandelbrot set.
See also
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