|In many stochastic systems the estimation of the model parameters is complicated by the fact that we do not observe these processes themselves, but rather a mapping or transform of them. One could think of a stochastic process that models the number of molecules of substrates in a biochemical reaction network within a growing cell for which we want to infer how unobserved cellular processes determine the intensity of the counting process. We consider the phenomena that can be modelled as infinite-server queues that are intrinsically more complex than the well-studied M/G/infinity queue. For instance, we consider models with Markov-modulated arrival rates, and models that take into account feedback. Hidden Markov models naturally play a role in the analysis. Of interest is the estimation of the model parameters and asymptotic properties of the estimators. In addition, we are interested in the non-parametric estimation of service time distributions in case of general service times.
|Mathisca de Gunst (VU), Bartek Knapik (VU), Michel Mandjes (UvA)
VU University Amsterdam (VU)