We consider a single class open queueing network, also known as a Generalized Jackson Network (GJN). A classical result in heavy traffic theory asserts that the sequence of normalized queue length processes of the GJN converge weakly to a Reflected Brownian Motion (RBM) in the orthant, as the traffic intensity approaches unity. However, barring simple instances, it is still not known whether th...

We consider a single class open queueing network, also known as a generalized Jackson network (GJN). A classical result in heavy-traffic theory asserts that the sequence of normalized queue length processes of the GJN converge weakly to a reflected Brownian motion (RBM) in the orthant, as the traffic intensity approaches unity. However, barring simple instances, it is still not known whether th...

This paper considers the queue length distribution in a class of FIFO single-server queues with (possibly correlated) multiple arrival streams, where the service time distribution of customers from each arrival stream may differ from one another among streams. It is widely recognized that the queue length distribution in a FIFO queue with multiple non-Poissonian arrival streams having different...

We analyze the structure of stochastic dynamics near either a stable or unstable fixed point, where the force can be approximated by linearization. We find that a cost function that determines a Boltzmann-like stationary distribution can always be defined near it. Such a stationary distribution does not need to satisfy the usual detailed balance condition but might have instead a divergence-fre...

A new bound is proposed relating the average cost sub-optimality of a myopic policy to its differential cost error. The bound is an expectation over the stationary distribution for this policy. For small queueing networks, we compute or approximate the distribution using the matrix analytic method and numerically test the bound. Product form approximations to the stationary distribution are als...

We illustrate several recent results on efficient estimation for semiparametric time series models with two types of AR(1) models: having independent and centered innovations, and having general and conditionally centered innovations. We consider in particular estimation of the autoregression parameter, the stationary distribution, the innovation distribution, and the stationary density.

In this paper we consider discrete-time multidimensional Markov chains having a block transition probability matrix which is the sum of a matrix with repeating block rows and a matrix of upper-Hessenberg, quasi-Toeplitz structure. We derive sufficient conditions for the existence of the stationary distribution, and outline two algorithms for calculating the stationary distribution.

We define an analog of Plancherel measure for the set of rooted unlabeled trees on n vertices, and a Markov chain which has this measure as its stationary distribution. Using the combinatorics of commutation relations, we show that order n steps are necessary and suffice for convergence to the stationary distribution.

We define an analog of Plancherel measure for the set of rooted unlabeled trees on n vertices, and a Markov chain which has this measure as its stationary distribution. Using the combinatorics of commutation relations, we show that order n2 steps are necessary and suffice for convergence to the stationary distribution.

This paper provides series expansions of the stationary distribution of a finite Markov chain. This leads to an efficient numerical algorithm for computing the stationary distribution of a finite Markov chain. Numerical examples are given to illustrate the performance of the algorithm.