[Ifeffit] Question about statistics

[Matt Newville]

Olga,

I'm having a very difficult time understanding what you are trying to
say and do. Lengthy emails aren't necessarily helpful.  Please try to
state simple questions and post simple examples.  Please understand
that you want us to read your messages, understand them, and respond
to them with something other than requests for clarification.  The
risk for you is that we'll simply give up asking for clarification
after a few attempts.  I've made a few attempts already.

Much of what you say is really very confusing.  Some of it is just
wrong (Bayesian approaches certainly do not dictate what "space" to
use).  It's also simply too much to reply to all of it.

It is clear that you're trying to do some fitting in k-space AND have
a meaningful statistical treatment. Stop doing this.  Fit in R-space
if you want to do any meaningful statistical analysis.   You also want
to do something "Bayesian" because some fit "didn't work", which you
don't actually define.  If a fit doesn't give meaningful results,  it
seems likely to me that the data simply doesn't support the variables
you're trying to fit.   I doubt a Bayesian approach is likely to help.
  Of course, you can use priors to skew the fit to expected values for
the parameters, but that's probably not very different than just
constraining parameter values.

If you have any questions about the code or statistical treatments, or
want to use something supported or for which development can possibly
progress, use Larch.   Ifeffit is no longer supported and won't be
developed further.  Specifically for you,  Ifeffit may very well have
bugs in fitting in k-space -- it's never been a good idea to fit in
k-space, so this option was never tested well.

If you're interested in adding restraints or priors or other Bayesian
tools to Larch, that would be great.    The fitting code in Larch is
already far superior to that in Ifeffit, but there is always room for
improvement.  There are a handful of Python tools for Bayesian
analysis, and also for using MCMC methods.   These are well-supported
and tested, and would not be hard to incorporate into Larch.

Cheers,

--Matt