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b values of the Gutenberg-Richter relation, and the ?- parameter of changes in mean event size. In our method,
two empirical probability densities for each parameter, those associated with target events (conditional density
distributions) and those not associated with them (background density distributions), are defined and assumed to
have a normal distribution. Therefore, three parameters are transformed by appropriate relations so that new parameters
are normally distributed. The retrospective analysis in the learning period and the prospective test of
testing period demonstrated that the proposed model performs better by about 0.1 units in terms of the information
gain per event than the value summed up with those of the three parameters. The results are confirmed by
a simulation with randomly selected model parameters.
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