Abstract
The problem of filter-based state estimation for a partially observed stochastic network is considered in this paper, using the measure change approach. The network is assumed to have two types of nodes: observed and hidden. Their dynamics are defined by a set of counting processes with state-dependent intensities. The goal is to derive the nonlinear optimal filter and to propose a numerical scheme for its practical implementation. Network models that allow the optimal filter to be finite-dimensional are also considered. The theoretical results are applied to a retrial queuing system to track changes in two hidden stations: one accumulates blocked customers and the other contains unsatisfied customers.
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Semenikhin, K.V. (2021). State Estimation in Partially Observed Stochastic Networks with Queueing Applications. In: Piunovskiy, A., Zhang, Y. (eds) Modern Trends in Controlled Stochastic Processes:. Emergence, Complexity and Computation, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-76928-4_7
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DOI: https://doi.org/10.1007/978-3-030-76928-4_7
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