This tutorial explains four ways to explore the uncertainties of two predictions made by a relatively simple, fast-running model. These are: Linear analysis Sampling a
intro_to_pyemu Intro to pyEMU¶ This notebook provides a quick run through some of the capabilities of pyemu for working with PEST(++). This run through is
Introduction to Regression¶ This tutorial proves an overview of linear regression. It illustrates fitting a polynomial to noisy data, including the role of SSE
In contrast to linear uncertainty analysis, non-linear methods do not suffer from the limitation of assuming a linear relationship between model predictions and model parameters.
A variance-covariance matrix, often referred to as a covariance matrix, is a square matrix that provides covariances between pairs of elements of a random vector.
The present tutorial addresses the ability (or otherwise) of yet-ungathered data to reduce the uncertainties of decision-critical predictions using linear analysis utilities from the PEST
Linear uncertainty analysis is also known as “first order second moment” (or “FOSM”) analysis. It provides approximate mathematical characterisation of prior predictive probability distributions, and
This is the first in a series of tutorials which demonstrate workflows for parameter estimation and uncertainty analysis with the PEST/PEST++ suites. These are not the only
OLPROC is a model dancing partner. Its role is to postprocess model outputs in order to match them with field measurements, as well as to
This tutorial demonstrates several options for spatial parameterization of linear and polylinear features. In a groundwater model, these may represent entities such as streams, rivers,
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.