** Non-Linear
Curve Fits**

**INTRODUCTION**

Frequently we have a table of values as a function of a single variable which may have measurement noise or other inaccuracies. These would be of the form

What we want to do then is to find the “m” parameters “λ” of some function f(x; λ) which is “non-linear” in the parameters and provides the best fit to the tabulated data.

** **

**NON-LINEAR FITS**

Given some function, then at each data point we can write

for the “m” parameters

and where “” represents the uncertainty or measurement error as a consequence of an imperfect initial estimate of the parameters. We can then approximate the errors as a linear combination of corrections to our parameter set as

where which could be used to iteratively update the original set as

Writing out the entire matrix we get

or more concisely

But this is an over determined set of equations and not in form allowing for a unique determination of the parameter corrections, . So we multiply each side by the transpose of the matrix “A” which switches rows and columns as

So that

where

and for each element

And also by recalling

then

or finally

which allows for a unique solution for values.

**GAUSSIAN FUNCTION**

The well known Gaussian distribution (also called the “Normal” distribution or Bell curve) has the general functional form

where the three parameters are given by

In this parameter set, A is area under the curve of G(x). The arithmetic average or mean is . And the last parameter σ is the standard distribution. These are further described in the Appendix: Gaussian Parameters.

We can write the derivatives of the Gaussian with respect to its three parameters as follows

The iterative corrections to the initial guess for the three parameters can be expressed in the equation

or in detail, this expands to the following

For the measurements of

then the individual matrix coefficients are

And defining

then the matrix elements are

We could solve the matrix equation using Gauss-Jordan but for only three parameters it is easier to write the solutions directly as

And this allows us to iteratively update the initial guesses for the parameters

** **

**APPENDIX: GAUSSIAN PARAMETERS**

** **

For a random variable, **X**, perhaps with a Gaussian probability
distribution, the results of “n” independent measurements can be represented as

The arithmetic average or mean of this series of “n” values is simply given by

and the variance (or square of the standard distribution) for a large number of “n” trials can be written

These equations are exact and work well for a small number of samples. But as the number of samples increase, it is easier to create a histogram.

Basically the data is grouped into a set of “m” bins where “y_{j}”
represents the number of values which lie within some small range, “Δx”, around a midpoint of “x_{j}”.
Each small range represents a separate bin and can be drawn as

As before the expression for the mean is

and the variance can be written as

As the number of discrete bins increases and the width “Δx” of each gets smaller, then the bins come to resemble a continuous curve as follows:

For a continuous curve the area under the Gaussian is given by

The mean value can then be written as

And the variance is given by

**APPENDIX: INITIAL ESTIMATE**

For a discrete set of data points

then if we first define

then the arithmetic average or mean value of the distribution is given by

and the variance or square of the standard deviation is given by

But since we are linear in A, we can write

where

then the total error associated with an initial guess for A is

and we can minimize A in a least squares sense by setting the derivative to zero

or