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A Python implementation of an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler for parameter estimation.

Project description

Approximate Bayesian computation (ABC) and so called “likelihood free” Markov chain Monte Carlo techniques are popular methods for tackling parameter inference in scenarios where the likelihood is intractable or unknown. These methods are called likelihood free as they are free from the usual assumptions about the form of the likelihood e.g. Gaussian, as ABC aims to simulate samples from the parameter posterior distribution directly. astroABC is a python package that implements an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler as a python class. It is extremely flexible and applicable to a large suite of problems. astroABC requires NumPy,``SciPy`` and sklearn. mpi4py and multiprocessing are optional.

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