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approximate bayesian computing with population monte carlo

Project Description

A Python Approximate Bayesian Computing (ABC) Population Monte Carlo (PMC) implementation based on Sequential Monte Carlo (SMC) with Particle Filtering techniques.

The abcpmc package has been developed at ETH Zurich in the Software Lab of the Cosmology Research Group of the ETH Institute of Astronomy.

The development is coordinated on GitHub and contributions are welcome. The documentation of abcpmc is available at readthedocs.org and the package is distributed over PyPI.

Features

  • Entirely implemented in Python and easy to extend

  • Follows Beaumont et al. 2009 PMC algorithm

  • Parallelized with muliprocessing or message passing interface (MPI)

  • Extendable with k-nearest neighbour (KNN) or optimal local covariance matrix (OLCM) pertubation kernels (Fillipi et al. 2012)

  • Detailed examples in IPython notebooks

History

0.1.2 (2016-01-27)

  • Added support for sampling with multiple distance simultaneously
  • Clean setup.py
  • Simplifying the code
  • Improved documentation

0.1.1 (2015-05-03)

  • Python 3 support
  • Minor fixes
  • Improved documentation

0.1.0 (2015-04-28)

  • First release
Release History

Release History

This version
History Node

0.1.2

History Node

0.1.1

History Node

0.1.0

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abcpmc-0.1.2.tar.gz (872.3 kB) Copy SHA256 Checksum SHA256 Source Jan 27, 2016

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