Name
Title | ||
|---|---|---|
Efficient importance sampling heuristics for the simulation of population overflow in Jackson networks |
Abstract | ||
|---|---|---|
In this paper we propose state-dependent importance sampling heuristics to estimate the probability of population overflow in Jackson networks with arbitrary routing. These heuristics approximate the "optimal" state-dependent change of measure without the need for costly optimization involved in other recently proposed adaptive algorithms. Experimental results on tandem, feed-forward and feed-back networks with a moderate number of nodes yield asymptotically efficient estimates (often with bounded relative error) where no other state-independent importance sampling techniques are known to be efficient.
|
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1225275.1225281 | ACM Transactions on Modeling and Computer Simulation (TOMACS) |
Keywords | DocType | Volume |
relative error,queueing theory,importance sampling,feed forward,jackson network | Journal | 17 |
Issue | ISSN | ISBN |
2 | 1049-3301 | 978-0-7803-9519-0 |
Citations | PageRank | References |
6 | 0.56 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor F. Nicola | 1 | 468 | 84.45 |
| Tatiana S. Zaburnenko | 2 | 12 | 1.72 |