Speaker:
Professor Dave Woods, Southampton Statistical Sciences Research Institute, University of Southampton

Abstract: Statistically designed experiments are the “gold standard” for learning about products, processes, and systems through the collection of data. By deliberately introducing controlled variability, whilst working to minimise uncontrolled variation, we can establish causal relationships, screen for important variables and build predictive models.
I will describe how design of experiments and statistical modeling can go beyond the usual factorial design and response surface methodology. In particular, I will present methodology for (i) experiments with dynamic input variables; (ii) design to learn unknown parameters in empirical and first-principle nonlinear models; and (iii) Bayesian nonparametric learning through sequential experimentation. Where possible, methods will be illustrated on relevant examples.