Introduction to Bayesian methods and OpenBUGS

In this block we introduce Bayesian methods, first, by giving the definitions of its main elements, and second, by means of some simple but illustrative examples. The concept of prior and posterior distributions are introduced and the formula to obtain the posterior, in terms of the prior and the sample likelihood is given. Prior and posterior predictive distributions are also defined and obtained. Some graphical examples are used to illustrate the new and deep concepts. The Doodle tool, to build the Bayesian network defining the models and their parameters is described in detail and some examples are given. Finally, an initial view of OpenBUGS is given using the BPR traffic model, which is converted from deterministic to random.