  A number of methods of flare prediction rely on classification of physical
characteristics of an active region, in particular optical classification of
sunspots, and historical rates of flaring for a given classification. However
these methods largely ignore the number of flares the active region has already
produced, in particular the number of small events. The past history of
occurrence of flares (of all sizes) is an important indicator to future flare
production. We present a Bayesian approach to flare prediction, which uses the
flaring record of an active region together with phenomenological rules of
flare statistics to refine an initial prediction for the occurrence of a big
flare during a subsequent period of time. The initial prediction is assumed to
come from one of the extant methods of flare prediction. The theory of the
method is outlined, and simulations are presented to show how the refinement
step of the method works in practice.
