Prognosis Using Gaussian Process Model

Abstract

This chapter introduces a nonparametric approach for longitudinal data modeling and prediction. This approach is based on the Gaussian process (GP) model. GP is a stochastic process and can be viewed as a distribution over functions with a continuous domain, e.g. time or space. The chapter also introduces the structure of the GP model. It discusses the estimation and prediction methods for the GP model, examines the pairwise GP model. The chapter presents the extension of a single output GP model to the general Multiple Output Gaussian Process (MOGP) model, which plays a critical role in longitudinal data prediction. It discusses the time-to-failure distribution based on the MOGP model. The chapter provides the basic structure of MOGP and explains the MOGP based prediction method. The Gaussian process, also known as Kriging method, is a very flexible yet effective nonparametric model to describe smooth functional data.