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pymcmcstat: A Python Package for Bayesian Inference Using Delayed Rejection Adaptive Metropolis

Many scientific problems require calibrating a set of model parameters to fit a set of data. Various approaches exist for performing this calibration, but not all of them account for underlying uncertainty within the problem. Examples of this uncertainty include random noise within experimental measurements as well as errors due to model discrepancy; i.e., missing physics in the model. A Bayesian framework provides a natural perspective from which to perform model calibration to accommodate this uncertainty. To utilize this approach, we make several assumptions regarding the problem.