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Python module for uncertainty quantification using a Markov chain Monte Carlo sampler

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MCMCPy - Markov Chain Monte Carlo Sampling with Python

Python module for uncertainty quantification using a Markov chain Monte Carlo sampler.

MCMCPy is a wrapper around the popular PyMC package (https://github.com/pymc-devs/pymc) for Python 2.7. The purpose of the MCMCPy module is to (1) standardize the format of the input and output of the underlying PyMC code and (2) reduce the inherent complexity of PyMC by pre-defining a statistical model of a commonly-used form. The MCMCPy module was originally released as part of the SMCPy code (https://github.com/nasa/SMCPy), but, in some cases, it is possible to isolate MCMCPy and use it directly without calling SMCPy's primary module.

To operate MCMCPy, the user supplies a computational model built in Python 2.7, defines prior distributions for each of the model parameters to be estimated, and provides data to be used for calibration. These are roughly the same steps required to operate SMCPy. Markov chain Monte Carlo sampling can be conducted with ease through instantiation of the MCMCSampler class and a call to the sample() method. The output of this process is an approximation of the parameter posterior probability distribution conditioned on the data provided.

This software was funded by and developed under the High Performance Computing Incubator (HPCI) at NASA Langley Research Center.


Notices: Copyright 2018 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. No copyright is claimed in the United States under Title 17, U.S. Code. All Other Rights Reserved.

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