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

## TL;DR

This paper introduces a generalized Gaussian process model tailored for binary time series data in computer experiments, enabling better analysis of complex biological processes like cell adhesion.

## Contribution

The paper develops a flexible GP model for binary responses with a novel mean function for time series, along with asymptotic theory and predictive methods.

## Key findings

- Model effectively captures biological information in cell adhesion data.
- Simulation studies demonstrate the model's predictive accuracy.
- Application reveals insights not observable in lab experiments.

## Abstract

Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments.