ABSTRACT
In the previous chapter we provided an overview of various R programming tools that will be needed when we start developing methods of model estimation. We also introduced the foremost probability and cumulative density functions that will be used in maximum likelihood estimation and simulation. Statistical modelling rests upon underlying probability functions, or mixtures of them, which are conceived to describe particular data situations. In this chapter we shall describe the relationship of data to probability and to likelihood, and show how these are in turn related to fitting and interpreting statistical models.
