Our previous study proposed a Bayesian framework to enhance the approach
by the renewal process to forecast earthquakes’ timing and tested it in
simulated seismicity-like time series. As a first step toward applying
the Bayesian approach to actual seismic activity, it is crucial to use
seismic catalogs for examination of the probability density functions in
Bayes’ theorem, which is in its simplest form in the Bayesian approach:
the inter-event time distribution and the conditional and inverse
probability between inter-event times at two cutoff magnitudes. In this
study, I examined the properties of these probability density functions
using time series with weak inter-event correlations extracted from
three seismic catalogs: stationary time series with a nearly constant
occurrence rate and aftershock sequences transformed by the Omori Utsu
law. I found a new scaling property related to the temporal hierarchy of
seismic activity. Using this property, I derived the above three
probability density functions. Regarding the inter-event time
distribution, I discuss its approximate scaling universality from the
viewpoint of temporal fluctuations of transformed seismic time series by
instantaneous occurrence rate. The derived inverse probability enables
probabilistic evaluation of the large earthquakes’ timing in the
simplest Bayesian approach. Finally, I discuss extending it to the
general Bayesian approach toward its practical use.