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