2.5 Further reading
There are a variety of techniques being explored to improve the flexibility and scalability of GPs – such as Deep GPs or Sparse GPs. The following resources explore some of these topics, and also provide a more thorough treatment of the content covered in this chapter:
Bayesian Analysis with Python, Martin: this book comprehensively covers core topics in statistical modeling and probabilistic programming, and includes practical walk-throughs of various sampling methods, as well as a good overview of Gaussian processes and a variety of other techniques core to Bayesian analysis.
Gaussian Processes for Machine Learning, Rasmussen and Williams: this is often considered the definitive text on Gaussian processes, and provides highly detailed explanations of the theory underlying Gaussian processes. A key text for anyone serious about Bayesian inference.
