Abstract
We provide an exact, continuous time extension to previous work in anticorrelated stochastic process simulation that was performed in an approximate, discrete time setting. These methods reduce the variance of continuous time Monte Carlo for Markov jump process systems. We rigorously construct antithetic Poisson processes and analytically prove the negative correlation between pairs. We then show how these anticorrelated Poisson processes can be used to drive Markov jump processes via a random time change representation. Finally, we provide a sufficient condition for variance reduction in the jump process context as well as demonstrate a simple example.
| Original language | English (US) |
|---|---|
| Article number | 7039916 |
| Pages (from-to) | 3401-3407 |
| Number of pages | 7 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 2015-February |
| Issue number | February |
| DOIs | |
| State | Published - Jan 1 2014 |
| Event | 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: Dec 15 2014 → Dec 17 2014 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
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Maginnis, P. A., West, M., & Dullerud, G. E. (2014). Exact simulation of continuous time Markov jump processes with anticorrelated variance reduced Monte Carlo estimation. Proceedings of the IEEE Conference on Decision and Control, 2015-February(February), 3401-3407. [7039916]. https://doi.org/10.1109/CDC.2014.7039916