A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors
This provides a more physically justified and parsimonious method for Gaussian process modeling in simulations, addressing a known bottleneck in handling mixed input types.
The paper tackles the problem of modeling computer simulations with both qualitative and numerical inputs by introducing a latent variable approach that maps qualitative factors to underlying numerical variables, resulting in superior predictive performance across various examples.
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.