ABSTRACT

Functional data analysis (FDA) – inference on curves or functions – has wide application in statistics. An example of considerable recent interest arises when considering computer models of processes; the output of such models is a function over the space of inputs of the computer model. The output is functional data in many contexts, such as when the output is a function of time, a surface, etc. A nonparametric Bayesian statistics
approach, utilizing separable Gaussian Stochastic Process as the prior distribution for functions, is a natural choice for smooth functions with a manageable (time) dimension. However, direct use of separable Gaussian stochastic processes is inadequate for irregular functions, and can be computationally infeasible for high dimensional functions. In this talk, we will develop and extend Bayesian FDA approaches for complex computer model validation, tailored to interdisciplinary problems in engineering and
the environment.