PEST provides a way of understanding what model calibration can achieve and what it cannot achieve.
PEST provides the means to get the most information possible out of your data and into your model.
It provides the means to assess the (often considerable) uncertainty associated with model parameters and predictions, even after a model has been calibrated.
PEST can tell you where model predictive uncertainty is coming from, and what data you need to gather to most effectively reduce it.
PEST implements both traditional parameter estimation based on the use of only a few parameters, as well as highly-parameterized, regularized inversion based on the use of hundreds (or even thousands) of parameters.
It implements both linear and nonlinear uncertainty analysis, including its unique, efficient and powerful "null-space Monte Carlo" methodology for rapid generation of many different calibration-constrained parameter fields.
PEST is supported by a plethora of utility software that expedites its use in conjunction with many widely-used environmental models.
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