Abstract We have built Prism, a Probabilistic Regression Instrument for Simulating Models. Prism 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, Prism can additionally be used as a stand-alone alternative to MCMC for model analysis, providing insight into the behavior of complex scientific models. With Prism, 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 Prism. 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 Prism and how one can combine it with MCMC methods to significantly speed up calibration processes (>15 times faster). Prism 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. Prism has also been reviewed by The Journal of Open Source Software.

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