A Gaussian process is a type of stochastic process that is particularly useful for Bayesian inference of complex problems. This book presents an overview of Gaussian process regression, all of the necessary theory, and real-world applications from the fields of medicine and engineering. It covers the required background in regression and examines key aspects of the modeling process, including model selection as well as more advanced topics, such as mixture models and kernel-based methods. For implementing the methods discussed in the text, the authors provide ad hoc software for download on their website.