31. Overview of other topics

In this part we collect additional material that does not fit directly into previous categories and may be considered unessential in an introductory treatment of Bayesian statistics and machine learning. The chapters are:

• Emulators, which are fast & accurate computer models of expensive theoretical simulations. Bayesian inference often requires a large number of samples of the simulations, e.g., for parameter estimation, which could be prohibitive unless we can use emulators to generate the samples.

• Dimensionality reduction gives an overview of singular value decompositions (SVD), which is a key component of dimensional reduction methods such as principal component analysis (PCA).