Four Ways to Explore Model Predictive Uncertainty

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 linearised posterior covariance matrix
• Iterative ensemble smoother
• Data space inversion

In doing this tutorial, you get to use the following programs:

• PEST
• PEST_HP
• PESTPP-IES
• DSI1
• Other members of the PEST utility support suite