Model Dispersion with prism: An Alternative to MCMC for Rapid Analysis of Models

Abstract

We have built P rism , a Probabilistic Regression Instrument for Simulating Models . P rism uses the Bayes linear approach and history matching to construct an approximation (“emulator”) of any given model by combining limited model evaluations with advanced regression techniques, covariances, and probability calculations. It is designed to easily facilitate and enhance existing Markov chain Monte Carlo (MCMC) methods by restricting plausible regions and exploring parameter space efficiently. However, P rism can additionally be used as a stand-alone alternative to MCMC for model analysis, providing insight into the behavior of complex scientific models. With P rism , the time spent on evaluating a model is minimized, providing developers with an advanced model analysis for a fraction of the time required by more traditional methods. This paper provides an overview of the different techniques and algorithms that are used within P rism . We demonstrate the advantage of using the Bayes linear approach over a full Bayesian analysis when analyzing complex models. Our results show how much information can be captured by P rism and how one can combine it with MCMC methods to significantly speed up calibration processes (>15 times faster). P rism is an open-source Python package that is available under the BSD 3-Clause License (BSD-3) at https://github.com/1313e/PRISM and hosted at  https://prism-tool.readthedocs.io . P rism has also been reviewed by The Journal of Open Source Software .