Hi,
This is quite a large task that would require a couple of months, but
it would mean that large scale screening of dispersion parameter
errors would have been possible. What is your plan for the
est_par_error branch
(http://svn.gna.org/viewcvs/relax/branches/est_par_error/?pathrev=25575)?
There are some useful things in there for the next developer to take
over - the new lib.dispersion.tsmfk01 gradient and Jacobian functions,
the sympy script for the partial derivatives, the TSMFK01 target
function class gradient methods, etc.
Regards,
Edward
On 2 September 2014 20:10, Troels E. Linnet
<NO-REPLY.INVALID-ADDRESS@xxxxxxx> wrote:
Update of task #7824 (project relax):
Priority: 3 - Low => 1 - Later
Status: In Progress => Gone walkabout
Percent Complete: 0% => 10%
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Follow-up Comment #50:
This is actually a quite hard work to implement, and will require a lot of
testing.
I therefore leave it, since I dont have time for it.
But it seemed that it is very possible indeed.
For the most simple model TSMFK01, reasonable results was achieved.
But seriously, perform MC calculations in the end.
Compared to R2eff errors, where the following analysis depend on the error,
the error estimation for the model parameters can wait to the very last end.
So it is not crucial.
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Re: [task #7824] Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.