How to Install and Uninstall python-bumps-doc Package on Kali Linux

Last updated: December 28,2024

1. Install "python-bumps-doc" package

Please follow the step by step instructions below to install python-bumps-doc on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                python-bumps-doc
            
                    
    
    Copied

2. Uninstall "python-bumps-doc" package

Please follow the guidelines below to uninstall python-bumps-doc on Kali Linux:


    
        
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                sudo apt remove
            
                        
                python-bumps-doc
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the python-bumps-doc package on Kali Linux

Package: python-bumps-doc
Source: python-bumps
Version: 0.9.1-2
Installed-Size: 7903
Maintainer: Debian Science Maintainers
Architecture: all
Depends: libjs-jquery, libjs-mathjax, libjs-sphinxdoc (>= 7.2.2)
Size: 2819076
SHA256: 9b74453f0ee571933a7cd3e5061dc9dde9e72d655f138228c41e5b945376eca2
SHA1: 0e6385204443ba8b7abbf52ebb92f53628c4108b
MD5sum: 70e0c11d38dce4e484ac97b3a985d2a7
Description: 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.
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This is the common documentation package.
Description-md5:
Multi-Arch: foreign
Homepage: https://github.com/bumps/bumps
Section: doc
Priority: optional
Filename: pool/main/p/python-bumps/python-bumps-doc_0.9.1-2_all.deb