We study bounds on the rate of convergence to the stationary distribution in monotone separable networks which are represented in terms of stochastic recursive sequences. Monotonicity properties of this subclass of Markov chains allow us to formulate conditions in terms of marginal network characteristics. Two particular examples, generalized Jackson networks and multiserver queues, are considered
We propose and analyze the convergence of a novel stochastic algorithm for monotone inclusions that ...
We apply recent results in Markov chain theory to Hastings and Metropolis algorithms with either ind...
In this paper, we investigate computable lower bounds for the best strongly ergodic rate of converg...
We study bounds on the rate of convergence to the stationary distribution in monotone separable netw...
A network belongs to the monotone separable class if its state variables are homogeneous and monoton...
Multiclass queueing networks (McQNs) extend the classical concept of the Jackson network by allowing...
In this paper we give bounds on the total variation distance from convergence of a continuous time p...
In a recent paper [5] it was shown that under suitable conditions stationary distributions of the (s...
In this paper, we give quantitative bounds on the $f$-total variation distance from convergence of a...
AbstractLet P be an infinite irreducible stochastic matrix, stochastically dominated by an irreducib...
For many real-life Bayesian networks, common knowledge dictates that the output established for the ...
International audienceWe illustrate through examples how monotonicity may help for performance evalu...
A stochastic matrix is "monotone" [4] if its row-vectors are stochastically increasing. Closure prop...
Abstract: This paper provides some properties of monotone functions of several variables. ...
For many real-life Bayesian networks, common knowledge dictates that the output established for the ...
We propose and analyze the convergence of a novel stochastic algorithm for monotone inclusions that ...
We apply recent results in Markov chain theory to Hastings and Metropolis algorithms with either ind...
In this paper, we investigate computable lower bounds for the best strongly ergodic rate of converg...
We study bounds on the rate of convergence to the stationary distribution in monotone separable netw...
A network belongs to the monotone separable class if its state variables are homogeneous and monoton...
Multiclass queueing networks (McQNs) extend the classical concept of the Jackson network by allowing...
In this paper we give bounds on the total variation distance from convergence of a continuous time p...
In a recent paper [5] it was shown that under suitable conditions stationary distributions of the (s...
In this paper, we give quantitative bounds on the $f$-total variation distance from convergence of a...
AbstractLet P be an infinite irreducible stochastic matrix, stochastically dominated by an irreducib...
For many real-life Bayesian networks, common knowledge dictates that the output established for the ...
International audienceWe illustrate through examples how monotonicity may help for performance evalu...
A stochastic matrix is "monotone" [4] if its row-vectors are stochastically increasing. Closure prop...
Abstract: This paper provides some properties of monotone functions of several variables. ...
For many real-life Bayesian networks, common knowledge dictates that the output established for the ...
We propose and analyze the convergence of a novel stochastic algorithm for monotone inclusions that ...
We apply recent results in Markov chain theory to Hastings and Metropolis algorithms with either ind...
In this paper, we investigate computable lower bounds for the best strongly ergodic rate of converg...
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