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  • Statistical inference for inter-arrival times of extreme events in . . .
    In many complex systems studied in statistical physics, inter-arrival times between events such as solar flares, trades and neuron voltages follow a heavy-tailed distribution The set of event times is fractal-like, being dense in some time windows and empty in others, a phenomenon which has been dubbed “bursty” A new model for the inter-exceedance times of such events above high
  • Markovian arrival process - Wikipedia
    The phase-type renewal process is a Markov arrival process with phase-type distributed sojourn between arrivals For example, if an arrival process has an interarrival time distribution PH with an exit vector denoted , the arrival process has generator matrix,
  • A Divide-and-Conquer Approach for Modeling Arrival Times in . . . - Springer
    A key component of BPS is the case-arrival model, which determines the pattern of new case entries into a process Although accurate case-arrival modeling is essential for reliable simulations—as it influences waiting and overall cycle times—existing approaches often rely on oversimplified static distributions of inter-arrival times
  • ilanavinnik Severe-Storm-Interarrival-Times-Study - GitHub
    This repository presents visualizations and supplementary materials from the published article "Modeling the Inter-Arrival Time Between Severe Storms in the United States Using Finite Mixtures" (Risks, 2025) The study analyzes storm inter-arrival times to evaluate the validity of the exponential distribution assumption in the context of a Poisson process
  • arXiv:2411. 07599v1 [math. PR] 12 Nov 2024
    This study leverages neural networks (NNs) to approximate the steady-state distribution of the marginal number of customers at each station in the tandem queueing system, based on the external inter-arrival and service time distributions
  • Modeling the Inter-Arrival Time Between Severe Storms in the . . . - MDPI
    When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate However, these assumptions are often violated in real-insurance applications When the rate at which events occur changes over time, the exponential distribution becomes unsuitable In this paper
  • Analysis of General Phase Type Inter-Arrival and Service Time . . .
    In this paper, a queueing system comprising of two-phase hyper-exponential inter-arrival time, Erlang-3 distributed service time, single server and infinite number of arrivals has been
  • Analyzing Impact of Different Inter-Arrival Time . . . - IEEE Xplore
    Poisson arrival-based analytical traffic models are found to be inappropriate for IoT networks Many empirical studies confirm that beta, gamma, generalized exponential, generalized Pareto, lognormal and Weibull distributions characterize the inter-arrival time of different types of IoT traffic It then becomes imperative to examine the impact of various arrival patterns on the quality of
  • L8-queuing-models-2023sp - MIT
    Give indications on how to improve the system’s performance Most closed-form results involve stationary regime (steady-state) and low-order moments (mean, variance) of the inter-arrival and service time distributions Trade-off: realistic model (few available results) vs tractability (assumptions are questionable)
  • Analysis of General Phase Type Inter-Arrival and Service Time . . .
    In this paper, a queuing system comprising of two- phase hyper-exponential inter-arrival time, Erlang-3 distributed service time, single server and infinite number of arrivals has been investigated in order to provide some insight into the analysis of phase type distributions queues





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