This chapter provides an overview of the classical Bayesian method for point estimation. The main point of departure of this method from other methods is that it considers the unknown x as a random variable. All the prior knowledge about this unknown is summarized in the form of a known prior distribution p(x) of x. If z is the set of observations that contains information about the unknown x, this distribution is often given in the form of a conditional distribution p(z│x). The basic idea is to combine these two pieces of information to obtain an optimal estimate of x, called the Bayes estimate.