Your message dated Thu, 16 Nov 2017 14:32:12 +0800
with message-id <1510813932.3379.7.ca...@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.
(NB: If you are a system administrator and have no idea what this
message is talking about, this may indicate a serious mail system
misconfiguration somewhere. Please contact ow...@bugs.debian.org
immediately.)
--
879829: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=879829
Debian Bug Tracking System
Contact ow...@bugs.debian.org with problems
--- Begin Message ---
Package: wnpp
Severity: wishlist
Owner: Drew Parsons <dpars...@debian.org>
* Package name : bumps
Version : 0.7.6
Upstream Author : Paul Kienzle <pkien...@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 ---
