7.1.2.
Discussing assumptions and theoretical background
7.1.2.1. Calibration in the context of VVUQ principle
7.1.2.2. Interest in the least square measurement
7.1.2.3. Introduction to Bayesian approach
Methodology
Navigation
Contents:
1. Glossary
2. Basic statistical elements
3. The Sampler module
4. Generating Surrogate Models
5. Sensitivity analysis
6. Dealing with optimisation issues
7. The Calibration module
7.1. Brief reminder of theoretical aspects
7.2. Using minimisation techniques
7.3. Analytical linear Bayesian estimation
7.4. Approximate Bayesian Computation techniques (ABC)
7.5. Markov chain Monte Carlo approach
7.6. CIRCE method
8. The Uncertainty modeler module
Related Topics
Documentation overview
7.
The Calibration module
7.1.
Brief reminder of theoretical aspects
Previous:
7.1.1.
Distances and likelihoods used to compare observations and model predictions
Next:
7.1.2.1.
Calibration in the context of VVUQ principle
