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Non-Gaussian observations such as binary responses are common in somecomputer experiments. Motivated by the analysis of a class of cell adhesionexperiments, we introduce a generalized Gaussian process model for binaryresponses, which shares some common features with standard GP models. Inaddition, the proposed model incorporates a flexible mean function that cancapture different types of time series structures. Asymptotic properties of theestimators are derived, and an optimal predictor as well as its predictivedistribution are constructed. Their performance is examined via two simulationstudies. The methodology is applied to study computer simulations for celladhesion experiments. The fitted model reveals important biological informationin repeated cell bindings, which is not directly observable in lab experiments.

A generalized Gaussian process model for computer experiments with binary time series