Bayesian refinement of solar flare prediction

Details Bayesian refinement of solar flare prediction Bayesian refinement of solar flare prediction

May 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004aas...204.5415w&link_type=abstract

American Astronomical Society Meeting 204, #54.15; Bulletin of the American Astronomical Society, Vol. 36, p.760

Mathematics

Logic

Scientific paper

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 how many 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 past history of flaring of an active region together with phenomenological rules of flare statistics to refine an initial prediction for flaring. The initial prediction may come from one of the extant prediction schemes, and appears in the method as a prior probability distribution. The theory of the new method is outlined, and simulations are presented to show how the refinement step works in practice. Construction of appropriate prior distributions is also discussed.
The author is supported by an Australian Research Council QEII Fellowship.

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