Abstract

Computer experiments have led to a growth in the development of certain types of experimental design which fill out the input space of a simulator in a comprehensive way: Latin Hypercube Sampling, Sobol sequences and many others. They differ from more traditional factorial experimental designs which have been used typically to fit polynomial response surfaces. Despite this structural difference the principals of good and indeed optimal design still apply as do the tensions between general purpose designs and designs tuned to particular models and utility functions.

The talk will be split between the fundamental principles of experimental design as applied to computer experiments and a review of notable methods from the research community. Some attention will be given to designs based on information theoretic principles and the connection to more general theories of learning, where Bayesian principles are particularly useful.