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Bug#879829: marked as done (ITP: bumps -- data fitting and Bayesian uncertainty modeling for inverse problems)

Your message dated Thu, 16 Nov 2017 14:32:12 +0800
with message-id <1510813932.3379.7.camel@debian.org>
and subject line Re: ITP: bumps -- data fitting and Bayesian uncertainty modeling for inverse problems
has caused the Debian Bug report #879829,
regarding ITP: bumps -- data fitting and Bayesian uncertainty modeling for inverse problems
to be marked as done.

This means that you claim that the problem has been dealt with.
If this is not the case it is now your responsibility to reopen the
Bug report if necessary, and/or fix the problem forthwith.

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-- 
879829: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=879829
Debian Bug Tracking System
Contact owner@bugs.debian.org with problems
--- Begin Message --- 
Package: wnpp
Severity: wishlist
Owner: Drew Parsons <dparsons@debian.org>

* Package name    : bumps
  Version         : 0.7.6
  Upstream Author : Paul Kienzle <pkienzle@nist.gov>
* URL             : https://github.com/bumps/bumps
* License         : BSD
  Programming Lang: Python
  Description     : data fitting and Bayesian uncertainty modeling for inverse problems

 Bumps is a set of routines for curve fitting and uncertainty analysis
 from a Bayesian perspective. In addition to traditional optimizers
 which search for the best minimum they can find in the search space,
 bumps provides uncertainty analysis which explores all viable minima
 and finds confidence intervals on the parameters based on uncertainty
 in the measured values. Bumps has been used for systems of up to 100
 parameters with tight constraints on the parameters. Full uncertainty
 analysis requires hundreds of thousands of function evaluations,
 which is only feasible for cheap functions, systems with many
 processors, or lots of patience.
 .
 Bumps includes several traditional local optimizers such as
 Nelder-Mead simplex, BFGS and differential evolution. Bumps
 uncertainty analysis uses Markov chain Monte Carlo to explore the
 parameter space. Although it was created for curve fitting problems,
 Bumps can explore any probability density function, such as those
 defined by PyMC. In particular, the bumps uncertainty analysis works
 well with correlated parameters.
 .
 Bumps can be used as a library within your own applications, or as a
 framework for fitting, complete with a graphical user interface to
 manage your models.


bumps is a prerequisite for SasView, ITP#879812

--- End Message ---
--- Begin Message --- 
uploaded to unstable

python-bumps 0.7.6-2

--- End Message ---
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