approximate bayesian computing with population monte carlo
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.
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
A 2D gauss case study
A Multi distance case study
A toy model including a comparison to theoretical predictions
Added support for sampling with multiple distance simultaneously
Simplifying the code
Python 3 support
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