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Bayesian control of the number of servers in a GI/M/c queueing system
(Elsevier, 2007)
In this paper we consider the problem of designing a GI/M/c queueing system. Given arrival and service data, our objective
is to choose the optimal number of servers so as to minimize an expected cost function which depends ...
Bayesian estimation for the M/G/1 queue using a phase-type approximation
(Elsevier, 2004)
This article deals with Bayesian inference and prediction for M/G/1 queueing systems. The
general service time density is approximated with a class of Erlang mixtures which are phase-type
distributions. Given this phase-type ...
Queues in Reliability
(Wiley, 2007)
Queueing models can be useful in solving many complex reliability problems. Component
failures are usually interpreted as the arrival of customers and the repair or
replacement of failed components is typically associated ...
An introduction to quadrature and other numerical integration techniques
(Wiley, 2007)
The objective in numerical integration is the approximation of a definite integral
using numerical techniques. There are a large number of numerical integration methods
in the literature and this article overviews some of ...
Bayesian prediction of the transient behaviour and busy period in short and long-tailed GI/G/1 queueing systems
(Elsevier, 2007)
Bayesian inference for the transient behavior and duration of a busy period in a single server queueing
system with general, unknown distributions for the interarrival and service times is investigated. Both
the interarrival ...
Bayesian estimation of ruin probabilities with heterogeneous and heavy-tailed insurance claim size distribution
(2007-08-09)
This paper describes a Bayesian approach to make inference for risk reserve processes with unknown claim size distribution. A flexible model based on mixtures of Erlang distributions is proposed to approximate the special ...
Bayesian estimation of the Gaussian mixture GARCH model
(Elsevier, 2007)
Bayesian inference and prediction for a generalized autoregressive conditional heteroskedastic (GARCH) model where the
innovations are assumed to follow a mixture of two Gaussian distributions is performed. The mixture ...