Background and Motivation
- Lecture 1: Motivation and Prototypical Examples (PDF)
Probability and Statistics Background
- Lectures 2-4: Statistical models and interval estimators (PDF)
- Supplemental Material: Random variables, estimators and sampling distributions (PDF)
Representation of Random Inputs
- Lectures 5-7: Representation of random variables and random fields (PDF)
- Revised Version of Chapter 5 (PDF)
Parameter Selection Techniques
- Lectures 8-11: Local and Global Sensitivity Analysis (PDF)
- Lecture 12-13: Active subspaces (PDF)
- Saltelli Implementation Algorithm (PDF)
Statistical Model Calibration
- Lectures 13-14: Frequentist techniques for model calibration (PDF)
- Lectures 15-18: Bayesian techniques for model calibration (PDF)
Uncertainty Propagation in Models
- Lecture 19: Linearly parameterized models (PDF)
- Lectures 19 and 20: Random sampling, perturbation methods and prediction intervals (PDF)
- Lectures 21-23: Stochastic spectral methods (PDF)
- Lectures 24-26: Sparse grid quadrature and interpolation (PDF)
- Lecture 26: Sparse grid example (PDF)
Surrogate Models
- Lectures 27-28: Surrogate and Reduced-Order Models (PDF)
Model Discrepancy and Active Subspace-Based Inference
- Lecture 29: Model discrepancey (PDF)
- Lecture 29: Active subspace-based Bayesian inference (PDF)
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