ABSTRACT
In this chapter, the basic concepts needed for the study of Bayesian and clas-
sical statistics will be described. In the first section, the most commonly used
statistical models are presented. They will provide the basis for the presen-
tation of most of the material of this book. Section 2.2 introduces the funda-
mental concept of likelihood function. Theoretically sound and operationally
useful definitions of measures of information are also given in this section.
The Bayesian point of view is introduced in Section 2.3. The Bayes theorem
acts as the basic rule in this inferential procedure. The next section deals
with the concept of exchangeability. This is a strong and useful concept as
will be seen in the following chapter. Other basic concepts, such as sufficiency
and exponential family, are presented in Section 2.5. Finally, in Section 2.6,
the multiparametric case is presented and the main concepts are revised and
extended from both the Bayesian and the classical points of view. Special
attention is given to the problem of parameter elimination in order to make
inference with respect to the remaining parameters.
