Exploring how to quantify both a mathematical model and the uncertainty, both its and yours.
- Physics -- Fluids, Genetics
- Human -- Economics, Social Networks
- Both -- Ecology, Epidemiology
- simulate "What If" scenarios
- explore behavior change, possible mitigations
- at times, short-term prediction -- http://lega.uazmath.org/?p=389
- approximation; fit a surface to it
- analyze parameter sensitivity (UQ)
- derivatives; necessarily a wide range?
- helps build our intuition about the whole system -- http://math.lanl.gov/~mac/papers/bio/HL03.pdf
- Input Parameters, x
- Outputs, F(x)
- Deterministic
- Stochastic
- Known Unknowns (Aleatoric / Accuracy)
- Measurer not accurate
- Generally normal distributions?
- Unknown Unknowns (Epistemic / Precision)
- Missing terms in model
- Maybe you don't know enough about your phenomenon
- cross-validate your model
- 10-fold, n-fold, all subsets?
- want parameters such that the model matches measurements
- minimize residuals, bootstrap