Background and Motivation
- Lecture 1: Motivation and Prototypical Examples (PDF)
Probability and Statistics Background
- Lectures 2 and 3: Random variables, estimators and sampling distributions (PDF)
Representation of Random Inputs
- Lecture 4: Representation of random variables and random fields
Parameter Selection Techniques
- Lecture 5: Parameter selection techniques (PDF)
- Lecture 6 and 7: Global Sensitivity Analysis (PDF)
Statistical Model Calibration
- Lectures 8 and 9: Frequentist techniques for model calibration (PDF)
- Lectures 10-12: Bayesian techniques for model calibration (PDF)
Uncertainty Propagation in Models
- Lectures 13 and 14: Random sampling, perturbation methods and prediction intervals (PDF)
- Lectures 15 and 16: Stochastic spectral methods (PDF)
- Lectures 17 and 18: Sparse grid quadrature and interpolation (PDF)
|