Abstract
This chapter evaluates Bayesian inference, which refers to the Bayesian statistical method for estimating the parameters of a model and for testing a hypothesis. It relies on subjective statistics and extensively uses Bayes’s theorem. In the early 1990s, Bayesian statistics boomed with the emergence of sampling techniques. These new tools rely on the computational power to sample from (rather than evaluate) the posterior probability. However, the main drawback of the Bayesian approach lies in the computation of the posterior probability. The analytical computation of the posterior probability is a complex problem for any application, and this has limited Bayesian statistics for years.
