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Re: Confused by nonlinear least square fitting example
- From: Joakim Hove <joakim dot hove at phys dot ntnu dot no>
- To: gsl-discuss at sources dot redhat dot com
- Cc: Simon <s dot martin at sheffield dot ac dot uk>
- Date: 29 Apr 2002 13:54:00 +0200
- Subject: Re: Confused by nonlinear least square fitting example
- References: <1020079484.1948.34.camel@dyn006240>
Simon <s.martin@sheffield.ac.uk> writes:
> I'm a newbie to GSL so please be nice if this is a dumb question.
So am I - but anyway.
> I don't get this. Surely it should point to a vector of guesses at the
> parameters that fit the model?
That was also my first reaction, I guess it is due to different ideas
of nomenclature.
In the Levenberg-Marquardt functions (and the example) the word
"parameter" is used to indicate "general purpose" information about
the dataset in question, whereas "x" in the fitting functions (expb_f
& expb_df in the example) is a vector of the unknown coefficients, and
*not* the independent variable of the dataset (to add to the confusion
I find the word parameter suitable for these coefficients).
All in all I sum up the GSL nomenclature in this way:
_Parameter_: Miscellaneous information about the dataset at hand.
_x_ : A vector of the unknown coefficient which we want to
determine.
Which is at least partly orthogonal to my intuitive understanding of
"param" and "x". Hopefully this did not only add to the confusion :-)
Joakim
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