Metadata-Version: 1.1
Name: astroabc
Version: 1.4.2
Summary: A Python implementation of an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler for parameter estimation.
Home-page: https://github.com/EliseJ/astroABC
Author: Elise Jennings
Author-email: elise.jennings@gmail.com 
License: MIT
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.
        		
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering
Requires: NumPy (>=2.7)
