  Computer simulations often involve both qualitative and numerical inputs.
Existing Gaussian process (GP) methods for handling this mainly assume a
different response surface for each combination of levels of the qualitative
factors and relate them via a multiresponse cross-covariance matrix. We
introduce a substantially different approach that maps each qualitative factor
to an underlying numerical latent variable (LV), with the mapped value for each
level estimated similarly to the correlation parameters. This provides a
parsimonious GP parameterization that treats qualitative factors the same as
numerical variables and views them as effecting the response via similar
physical mechanisms. This has strong physical justification, as the effects of
a qualitative factor in any physics-based simulation model must always be due
to some underlying numerical variables. Even when the underlying variables are
many, sufficient dimension reduction arguments imply that their effects can be
represented by a low-dimensional LV. This conjecture is supported by the
superior predictive performance observed across a variety of examples.
Moreover, the mapped LVs provide substantial insight into the nature and
effects of the qualitative factors.
