Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian nonparametric inference for the M/G/1 queueing systems based on
  the marked departure process   In the present work we study Bayesian nonparametric inference for the
continuous-time M/G/1 queueing system. In the focus of the study is the
unobservable service time distribution. We assume that the only available data
of the system are the marked departure process of customers with the marks
being the queue lengths just after departure instants. These marks constitute
an embedded Markov chain whose distribution may be parametrized by stochastic
matrices of a special delta form. We develop the theory in order to obtain
integral mixtures of Markov measures with respect to suitable prior
distributions. We have found a sufficient statistic with a distribution of a
so-called S-structure sheding some new light on the inner statistical structure
of the M/G/1 queue. Moreover, it allows to update suitable prior distributions
to the posterior. Our inference methods are validated by large sample results
as posterior consistency and posterior normality.

10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int