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We consider stochastic models in presence of uncertainty, originating from lack of knowledge of parameters or by unpredictable effects of the environment. We focus on population processes, encompassing a large class of systems, from queueing networks to epidemic spreading. We set up a formal framework for imprecise stochastic processes, where some parameters are allowed to vary in time within a given...
We compare population models in terms of Continuous Time Markov Chains with embedded deterministic delays (delayed CTMC), in which an exponential timed transition can only update the state of the system after a deterministicdelay, and delay differential equations (DDE). We prove a fluid approximation theorem, showing that, when the size of the population goes to infinity, the delayed CTMC converges...
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