The debate between the proponents of "classical" and "Bayesian"
statistica} methods continues unabated. It is not the purpose of the
text to resolve those issues but rather to demonstrate that within the
realm of actuarial science there are a number of problems that are
particularly suited for Bayesian analysis. This has been apparent to
actuaries for a long time, but the lack of adequate computing power and
appropriate algorithms had led to the use of various approximations. The
two greatest advantages to the actuary of the Bayesian approach are that
the method is independent of the model and that interval estimates are
as easy to obtain as point estimates. The former attribute means that
once one learns how to analyze one problem, the solution to similar, but
more complex, problems will be no more difficult. The second one takes
on added significance as the actuary of today is expected to provide
evidence concerning the quality of any estimates. While the examples are
all actuarial in nature, the methods discussed are applicable to any
structured estimation problem. In particular, statisticians will
recognize that the basic credibility problem has the same setting as the
random effects model from analysis of variance.