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Over the years, researchers have found that PNs are powerful in modeling events graphically and mathematically [13].
Regressors modeling events were convolved with a canonical Haemodynamic Response Function (HRF), and parameter estimates for all regressors were obtained at each voxel by maximum-likelihood estimation.
In order to model event patterns, a deep Gaussian mixture model (GMM) is constructed with observed normal events.
The context-dependent event priors are used to model event probabilities within the context.
Pareto distribution is more suited for modelling event arrival when idle periods are long [10].
Not least of these is that games are black-box systems affording little or no insight into how they work or how they model events.
The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used.
The integrated toolsets used to model Event-B is Rodin [2].
Venter said that biology will require even faster supercomputers in the future to model events within living organisms.
A PN is a directed graph used to model events and states in a distributed system [12, 13, 14].
Poisson distribution is suitable to model the event occurrence [49, 50] and has been widely used in analyzing routing protocols to model events whose time/place of occurrence is random and independent from each other [51 53].
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