Table of Contents

Introduction. Bayesian Nonlinear Regression with Gaussian Process Priors. Inference and Computation for Gaussian Process Regression Model. Covariance Function and Model Selection. Functional Regression Analysis. Mixture Models and Curve Clustering. Generalized Gaussian Process Regression for Non-Gaussian Functional Data. Some Other Related Models. Appendices. Bibliography. Index.

About the Author

Jian Qing Shi, Ph.D., is a senior lecturer in statistics and the leader of the Applied Statistics and Probability Group at Newcastle University. He is a fellow of the Royal Statistical Society and associate editor of the Journal of the Royal Statistical Society (Series C). His research interests encompass functional data analysis using covariance kernel, incomplete data and model uncertainty, and covariance structural analysis and latent variable models. Taeryon Choi, Ph.D., is an associate professor of statistics at Korea University. His research mainly focuses on the use of Bayesian methods and theory for various scientific problems.