The Annals of Applied Probability

Steady-state simulation of reflected Brownian motion and related stochastic networks

Jose Blanchet and Xinyun Chen

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This paper develops the first class of algorithms that enable unbiased estimation of steady-state expectations for multidimensional reflected Brownian motion. In order to explain our ideas, we first consider the case of compound Poisson (possibly Markov modulated) input. In this case, we analyze the complexity of our procedure as the dimension of the network increases and show that, under certain assumptions, the algorithm has polynomial-expected termination time. Our methodology includes procedures that are of interest beyond steady-state simulation and reflected processes. For instance, we use wavelets to construct a piecewise linear function that can be guaranteed to be within $\varepsilon$ distance (deterministic) in the uniform norm to Brownian motion in any compact time interval.

Article information

Ann. Appl. Probab., Volume 25, Number 6 (2015), 3209-3250.

Received: January 2012
Revised: September 2014
First available in Project Euclid: 1 October 2015

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Zentralblatt MATH identifier

Primary: 60J65: Brownian motion [See also 58J65] 65C05: Monte Carlo methods

Reflected Brownian motion steady-state simulation dominated coupling from the past wavelet representation


Blanchet, Jose; Chen, Xinyun. Steady-state simulation of reflected Brownian motion and related stochastic networks. Ann. Appl. Probab. 25 (2015), no. 6, 3209--3250. doi:10.1214/14-AAP1072.

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