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A Python Approximate Bayesian Computing (ABC) Population Monte Carlo (PMC) implementation based on Sequential Monte Carlo (SMC) with Particle Filtering techniques.
.. image:: https://raw.githubusercontent.com/jakeret/abcpmc/master/docs/abcpmc.png :alt: approximated 2d posterior (created with triangle.py). :align: center
The abcpmc package has been developed at ETH Zurich in the Software Lab of the Cosmology Research Group <http://www.cosmology.ethz.ch/research/software-lab.html> of the ETH Institute of Astronomy <http://www.astro.ethz.ch>.
The development is coordinated on GitHub <http://github.com/jakeret/abcpmc> and contributions are welcome. The documentation of abcpmc is available at readthedocs.org <http://abcpmc.readthedocs.org/> and the package is distributed over PyPI <https://pypi.python.org/pypi/abcpmc>_.
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 <http://nbviewer.ipython.org/github/jakeret/abcpmc/blob/master/notebooks/2d_gauss.ipynb>_ case study
A Multi distance <http://nbviewer.ipython.org/github/jakeret/abcpmc/blob/master/notebooks/dual_abc_pmc.ipynb>_ case study
A toy model <http://nbviewer.ipython.org/github/jakeret/abcpmc/blob/master/notebooks/toy_model.ipynb>_ including a comparison to theoretical predictions