Data fitting with bayesian uncertainty analysis
Project description
Bumps provides data fitting and Bayesian uncertainty modeling for inverse problems. It has a variety of optimization algorithms available for locating the most like value for function parameters given data, and for exploring the uncertainty around the minimum.
Installation is with the usual python installation command:
pip install bumps
Once the system is installed, you can verify that it is working with:
bumps doc/examples/peaks/model.py –chisq
Documentation is available at readthedocs
Release notes
v0.7.5.9 2016-04-22
population initializers allow indefinite bounds
use single precision criterion for levenberg-marquardt and bfgs
implement simple, faster, less accurate Hessian & Jacobian
compute uncertainty estimate from Jacobian if problem is sum of squares
gui: fit selection window acts like a dialog
v0.7.5.8 2016-04-18
accept model.par output from a different model
show residuals with curve fit output
only show correlations for selected variables
show tics on correlations if small number
improve handling of uncertainty estimate from curvature
tweak dream algorithm – maybe improve the acceptance ratio?
allow model to set visible variables in output
improve handling of arbitrary probability density functions
simplify loading of pymc models
update to numdifftools 0.9.14
bug fix: improved handling of ill-conditioned fits
bug fix: avoid copying mcmc chain during run
bug fix: more robust handling of –time limit
bug fix: support newer versions of matplotlib and numpy
miscellaneous tweaks and fixes
v0.7.5.7 2015-09-21
add entropy calculator (still unreliable for high dimensional problems)
adjust scaling of likelihood (the green line) to match histogram area
use –samples to specify the number of samples from the distribution
mark this and future releases with a DOI at zenodo.org
v0.7.5.6 2015-06-03
tweak uncertainty calculations so they don’t fail on bad models
v0.7.5.5 2015-05-07
documentation updates
v0.7.5.4 2014-12-05
use relative rather than absolute noise in dream, which lets us fit target values in the order of 1e-6 or less.
fix covariance population initializer
v0.7.5.3 2014-11-21
use –time to stop after a given number of hours
Levenberg-Marquardt: fix “must be 1-d or 2-d” bug
improve curvefit interface
v0.7.5.2 2014-09-26
pull numdifftools dependency into the repository
v0.7.5.1 2014-09-25
improve the load_model interface
v0.7.5 2014-09-10
Pure python release
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