Ubuntu
python-bumps package

python-bumps 0.7.13-1 source package in Ubuntu

python-bumps (0.7.13-1) unstable; urgency=medium

  * New upstream release.

 -- Drew Parsons <email address hidden>  Mon, 28 Oct 2019 10:46:55 +0800

Series	Pocket	Published	Component	Section

File	Size	SHA-256 Checksum
python-bumps_0.7.13-1.dsc	2.6 KiB	a94282f12bb04871229dbdb632e758786e7b6af43637ba3783633063f4726fe1
python-bumps_0.7.13.orig.tar.gz	3.2 MiB	c7070b55bdcb3ebb3bdb4242092c1f9c68ad8d7ecfe0990cd80b849d9f80f0a7
python-bumps_0.7.13-1.debian.tar.xz	12.5 KiB	a65503ce15d84a32048db623e509aece4dc2033978bd0cc1ef87ebedf8a20bc4

No changes file available.

bumps-private-libs: data fitting and Bayesian uncertainty modeling for inverse problems (libraries): 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.
.
This package installs the compiled libraries used by the Python modules.

python-bumps-doc: data fitting and Bayesian uncertainty modeling for inverse problems (docs): 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.
.
This is the common documentation package.

python3-bumps: data fitting and Bayesian uncertainty modeling for inverse problems (Python 3): 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.
.
This package installs the library for Python 3.