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Re: Fitting problem

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  • Subject: [mg95536] Re: Fitting problem
  • From: dh <dh at>
  • Date: Wed, 21 Jan 2009 06:44:32 -0500 (EST)
  • References: <gl4a9k$gk9$>


here is an attempt to tackle your problem:

We have one independent variable, time: t.

we have datasets




we have models with parameters p (not all models may need all parameters):




we define data interpolation functions that we will use to define an 

error function:




define an error function:

err[t_,p1_,..]:= (f1[t]-mod1[t,p1,..])^2 + (f2[t]-mod2[t,p1,..])^2+...

we define artificial zero data:


Finally we fit err against the zero data:


hope this helps, Daniel

Ktota wrote:

> Dear All ;),


> i have the following situation:


> 5 mathematical models describing the formation of different species.

> The models  (so the species) are interdependent, means.. they all have

> the same parameters but some of the have also other additional

> parameter. Example: Model 1 has a parameter "lifetime1"... model 2

> will include this parameter "liftime1" and a new parameter

> "lifetime2".


> My trouble is now to find best fit values for the parameters in all

> the models at the same time. I don't want to find a best fit for model

> 1 and then find out when doing best fit for model 2 that model 2 won't

> work with the parameter values found in the first run with model 1. So

> the fitting must consider all the models to find best fit values for

> the parameter.


> I wonder if this achievable with mathematica or if i would be better

> of with other software


> thank you for your help


>  Ktota



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