ABSTRACT
This chapter introduces the principles and basic tools of Bayesian statistical inference. It presents an approach to statistical inference that leads to making probability statements about unknown quantities of interest or events, for example the occurrence of a disease such as cancer in a particular patient, or the event of surviving at least 5 years after diagnosis with stage 3 breast cancer. The chapter begins with an elementary form of Bayes' theorem. Bayes' theorem follows from the mathematical definition of conditional probability. It discusses the concepts of continuous and discrete random variables. A continuous outcome in theory has a continuum of possible values, while a discrete outcome has a finite or countably infinite number of possible values.
