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Bug#879829: ITP: bumps -- data fitting and Bayesian uncertainty modeling for inverse problems

Package: wnpp
Severity: wishlist
Owner: Drew Parsons <dparsons@xxxxxxxxxx>

* Package name    : bumps
  Version         : 0.7.6
  Upstream Author : Paul Kienzle <pkienzle@xxxxxxxx>
* 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