Course Information


Local links


MA 797

Uncertainty Quantification for Physical and Biological Models

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)