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Okay, thanks.
Thanks Bobby.
Sorry, ignore that last x value.
I'm actually looking to learn the method for doing this, as I have many cases and many curve fits to do (the obs I've provided are just for one case).
Ooops, yes, sorry - for that equation F(x) tends to alpha, but I need it to tend to 1 for increasing x. Let's change the equation to:
My values that I'm trying to fit this to are:
x: 2.567,2.667,2.767,2.867,2.967,3.067
F(x): 1.562, 1.411, 1.287, 1.192, 1.10
with the condition as x-> inf, F(x) -> 1
From my limited knowledge, isn't F(X) my objective function? The data are observations, not generated by a formula, and the process being modelled isn't described by an empirical formula (other than the one I'm trying to fit).
When you refer to gradient, do you mean
? How do I find best values for alpha and beta?Many thanks
Hi
I'm learning about fitting a curve to a set of observations. The obs, F(x), decrease (inverse?) exponentially with increasing x, so that a suitable curve has the form:
so that F(x) tends to 1 as x tends to infinity. So I need to find the best values for alpha and beta to give the best fit. I'm a little new to this so would greatly appreciate advice on how to do this. I think that I will have to use a numerical minimisation routine, but not too sure how. A quick look at a suitable one hints that I may need to supply the derivative of the function. Would I just supply the derivative w.r.t x, or differentiate w.r.t. alpha and beta also?
Any help would be great.
Cheers
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