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Explore advanced queueing analytics methodologies in this 15-minute conference talk that delves into the intersection of machine learning and queueing theory. Learn about causal queueing approaches and discover SiMLQ (Simulation with Machine Learning for Queueing), a novel framework for data-driven simulation in queueing systems. Examine how modern analytical techniques can be applied to traditional queueing problems, with particular emphasis on leveraging data to enhance simulation accuracy and predictive capabilities. Gain insights into cutting-edge research that bridges theoretical queueing models with practical machine learning applications, presented as part of the 12th International Conference on Matrix-Analytic Methods in Stochastic Models at the Fields Institute.
