Description:

astroabc 1.5.0

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.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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