Bayesian Inference and Stochastic Processes

AbstractChapter 8 focus on a cornerstone of modern statistics, Bayesian inference. Here Bayesian inference is applied for description of autoregressive processes. After introducing the main concepts, examples applied to real data of temperature and CO2 concentration in Antarctica, as well as radar detection, are presented. Bayesian analysis of the Poisson process is presented with the waiting-time paradox. The Chapter ends with an application to lighthouse detection as a remarkable example of Bayesian inference.