ABSTRACT
Abstract We present a practical introduction to the application of Bayesian inferential methods for commonly used parametric models for extreme event data. We discuss the general theory of Bayesian inference and the choice of a prior distribution for extreme value parameters. We also examine practical implementation issues using the statistical package R (R Core Team, 2013). We explain the main concepts through a series of examples. In Section 13.1 we provide the theoretical background and give two examples using simulated data. In Section 13.2 we provide two additional data examples. The first uses a non-stationary model for athletics data. The second, available on the book website, uses a bivariate model for monthly temperature maxima at two sites in the Australian state of Victoria. The algorithms and the data are also available on the book website. The R code provided on the website enables the reader to fully examine and reproduce all the examples given in this chapter.
