Name
Abstract | ||
|---|---|---|
We describe two variance reduction methods for estimating the mean time to failure (MTTF) in Markovian models of highly reliable systems. The first method is based on a ratio representation of the MTTF and employs importance sampling. The second method is based on a hybrid simulation/analytic technique where the number of simulated transitions are reduced by computing partial results analytically. Experiments with a large example show the effectiveness of both techniques for highly reliable systems. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/WSC.1988.716207 | Winter Simulation Conference |
Keywords | Field | DocType |
importance sampling,failure simulation,hybrid simulation,variance reduction method,large example,reliable system,varaince reduction,markovian model,ratio representation,partial results analytically,mean time,analytic technique,mean time to failure,steady state,availability,redundancy,variance reduction,computational modeling,operations research,monte carlo methods,failure analysis | Mean time between failures,Importance sampling,Monte Carlo method,Markov process,Computer science,Simulation,Redundancy (engineering),Steady state,Variance reduction,Reliability engineering,Technical report | Conference |
ISBN | Citations | PageRank |
1-4244-1306-0 | 4 | 0.53 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Perwez Shahabuddin | 1 | 1364 | 181.65 |
| Victor F. Nicola | 2 | 468 | 84.45 |
| Philip Heidelberger | 3 | 2331 | 346.59 |
| Ambuj Goyal | 4 | 206 | 38.09 |
| Peter W. Glynn | 5 | 1527 | 293.76 |