## 1 Introduction  
The Pacific Ring of Fire is one of the most seismically active regions of the world. Alaska and western Canada are a part of this ring and are prone to the occurrence of significant earthquakes. This geographic region is characterized by high seismic activity and is capable of producing megathrust earthquakes. These earthquakes can pose significant hazard and are also capable of triggering tsunamis or intense ground shaking [1] and subsidiary hazards such as liquefaction, landslides and aftershocks [2]. While tsunamis pose a serious threat to coastal areas, ground shaking can cause damage to infrastructure and endanger human life. Therefore, it is important to perform a comprehensive statistical analysis of the aftershock sequences in the Aleutian subduction zone and central Alaska. Moreover, the occurrence of large aftershocks poses a significant risk to the infrastructure that has been affected by a mainshock. Therefore, estimating the probabilities for the occurrence of the largest expected aftershocks plays an important role in post-mainshock decision-making [3, 4]. One of the earliest empirical studies of the difference between the magnitude of the mainshock and its largest aftershock was conducted by [PERSON] [5], who postulated that the largest aftershock is on average 1.2 magnitudes lower than the mainshock regardless of the magnitude of the mainshock. [PERSON] [6; 7] proposed that the magnitude difference between the mainshock and the largest aftershock was independent of the number of events. [PERSON] and [PERSON] [8] were one of the first in developing an aftershock forecasting model. They introduced a parametric model that was capable of computing the probabilities of aftershocks in a certain time window after a mainshock for California. [PERSON] et al. [9] proposed the methodology which aforementioned model parameters can be estimated with Bayesian updating from both the ongoing aftershock sequence and from historic aftershock sequences. An important step in the calculation of the probability of having an earthquake above a certain magnitude is the estimation of the model parameters that describe the seismicity rate and the frequency-magnitude distribution. Those parameters are highly dependent on the lower cut-off magnitude, \(m_{0}\). The correct estimation of the cut-off magnitude plays a crucial role in earthquake forecasting and modelling. [PERSON] and [PERSON] [10] emphasized that a high-value of the cut-off magnitude can result in under-sampling of useful data and a low-value of the cut-off magnitude can result in uncertainty and bias of the estimated seismicity parameters and forecasting model, respectively. The other issue in aftershock forecast modelling is the catalogue incompleteness right after the occurrence of strong mainshocks [11; 12]. This early catalogue incompleteness can affect significantly the estimation of the parameters of the earthquake decay rate. The uncertainties in the estimation of the parameters of the aftershock decay rate can result in significant miscalculation of the probabilities for the occurrence of largest events. The empirical prior probability distribution was presented by [PERSON] et al. [13] to reduce the uncertainty of the parameter estimation of the ETAS model regarding the incompleteness of the earthquake catalogues. Utilizing generic parameters to create an aftershock forecast model for the early days after the mainshocks is one of the possible ways to control the catalogue incompleteness. [PERSON] et al. [14] introduced a method for generic parameter estimation by using tectonic zoning of [PERSON] et al. [15] to improve the spatial distribution of forecasted events. In this approach, Bayes' rule and aftershock records are used to update the generic parameters. In addition, the distribution of the regional generic parameters can be considered as a prior and the aftershock data can be used to calculate the posterior distribution. [PERSON] et al. [9] applied this approach to the 2018 Anchorage aftershock sequence.