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Error Propagation 
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#1
Nov2012, 11:59 AM

P: 825

I am confused about calculating errors. I have learned if you take the variance covariance matrix [itex]\Sigma_{ij}[/itex] of a fit of function f(x,p) to data for parameters [itex]p_i[/itex] (for example by using LevenbergMarquart) that the one sigma error interval for [itex]p_i[/itex] is [tex]\sigma_{p_i}=\sqrt{\Sigma_{ii}}[/tex] I only understand this, if there are no covariance terms. Why do we do this? I would have thought a better way to find the error would be to do diagonalize [itex]\Sigma[/itex], say the diagonal form is [itex]\Xi[/itex] with normalized eigenvectors [itex](\vec{v})_k[/itex]. Then we would have independent variables that have a Gaussian distribution and one can calculate the error on [itex]p_i[/itex] using error propagation, i.e. [tex]\sigma_{p_i} = \sqrt{\sum \Xi_{kk}\left\langle(\vec{v})_k\mid l_i \right\rangle}[/tex] where [itex]\left\langle(\vec{v})_k\mid l_i \right\rangle[/itex] is the [itex]i^\text{th}[/itex] component of [itex](\vec{v})_k[/itex]. If this is permissible, is there a name for it?



#2
Nov2012, 07:14 PM

Sci Advisor
P: 3,300

I'm not sure what you mean by "the variance covariance matrix [itex] \Sigma_{i,j} [/itex] of a fit of the function f(x,p) to the data for parameters [itex] p_i [/itex]". What is the definition of that matrix? 


#3
Nov2112, 03:59 PM

P: 825

Well I guess that you know the theory better than I do, but the idea is somehow a correspondence between least squares and maximum likelihood.
So you have the sum of the squares of a fit function [itex]f(x,p_1,p_2,\dots)[/itex] to data [itex]x_i,y_i[/itex] [tex] sq(p_1,p_2,\dots) = \sum_i (f(x_i,p_1,p_2,\dots)y_i)^2 [/tex] And the residuals [tex]r_i=f(x_i,p_1,p_2,\dots)y_i[/tex] for some optimal set of parameters [itex]p_k[/itex] that minimizes sq. If the residuals are gaussian then the variance of the residuals times the reciprocal of the Hessian of [itex]sq(p_1,p_2,\dots)[/itex] is somehow a measure of how confident one can be in the fitted parameters and it is also a variancecovariance matrix. This is how I understand it, but if I would really understand the theory I wouldn't be asking questions. Anyhow my question was why one only uses the diagonal elements of that matrix. 


#4
Nov2512, 12:53 PM

Sci Advisor
P: 3,300

Error Propagation
Can your original question can be considered outside of the context of curvefitting.



#5
Nov2612, 03:32 PM

P: 825

Exactly. Maybe the thing I am looking for already has a name. If we have a covariance matrix like this
[tex]\Sigma = \left( \begin{matrix} .1&100\\ 100&1000 \end{matrix} \right)[/tex] The first parameter is varying very little while the second one is varying a lot. But the second parameter also has a large influence on the first parameter, and it seems to me that this does not get captured if we use .1 as the variance for the first parameter. So I was suggesting to diagonalize the matrix to get independent parameters and then something like error propagation to determine the "real" uncertainty of the first parameter. I tried to make an example but I don't know how to make random numbers with a given covariance matrix. 


#6
Nov2612, 07:28 PM

Sci Advisor
P: 3,300

You could use a bivariate normal distribution and try to get the desired covariance matrix.
if you don't want to use the variance of a random variable to define its uncertainty, you'll have to state what definition for uncertainty that you want to use. The variance of one random variable in a joint distribution, doesn't define a joint confidence interval for several variables. Perhaps you are trying to find a joint confidence interval. 


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