PEST - Model-Independent Parameter Estimation and Uncertainty Analysis


We are pleased to provide consultancy services in model calibration and predictive uncertainty analysis.

Please contact us if you need help with any of the following:

Model calibration

We can help you to apply state-of-the-art regularized inversion to the calibration of your models. With regularized inversion, model-to-measurement fits are better and model predictive error variance is smaller than when using traditional calibration techniques. As much information as possible is extracted from expensive calibation datsets, while structural noise is reduced to minimum.

Software development

No graphical user interface or simulation program provides solutions for all modeling applications. We have developed programs to support the modeling process in a wide variety of settings. We can write custom software to enhance your modeling in specific application areas; assist you in processing environmental data; and allow you to most efficiently and effectively incorporate your data into the model calibration and predictive analysis processes.

Quantification of model predictive uncertainty

Where the cost of being wrong is high, and/or where decision-making must incorporate risk, model predictive uncertainty analysis is an essential component of model deployment. We can help you apply PEST's state-of-the-art uncertainty analysis functionality in your modelling contexts, and incorporate the outcomes of this analysis into the decision-making process.

Optimization of data acquisition

What is the best data to gather? It is that which reduces the uncertainty of key model predictions by the largest amount. PEST utility software is able to rank data that has not yet been gathered according to its efficacy in uncertainty reduction. We can help you apply this software in your modeling setting and, if necessary, enhance its capabilities for more effective use in your study areas.

Modeling for mediation and negotiation.

Dispute settlement through duelling models is archaic and unscientific. Application of PEST technology in conjunction with environmental models enables a quantitative assessment to be made of what can be said about the future based on the current environmental dataset, and what can only be guessed. This provides a far better basis for dispute resolution than competing, strongly mouthed, but untested assertions of "what will surely happen".

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