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Perfect Sampling of Markov Chains with Piecewise Homogeneous Events, arXiv (2010)
| Content Provider | CiteSeerX |
|---|---|
| Author | Gaujal, Bruno Pin, Furcy |
| Abstract | Perfect sampling is a technique that uses coupling arguments to provide a sample from the stationary distribution of a Markov chain in a finite time without ever computing the distribution. This tech-nique is very efficient if all the events in the system have monotonicity property. However, in the general (non-monotone) case, this technique needs to consider the whole state space, which limits its application only to chains with a state space of small cardinality. We propose here a new approach for the general case that only needs to consider two trajectories. Instead of the original chain, we use two bounding processes (envelopes) and we show that, whenever they couple, one ob-tains a sample under the stationary distribution of the original chain. We show that this new approach is particularly effective when the state space can be partitioned into pieces where envelopes can be easily com-puted. We further show that most Markovian queueing networks have this property and we propose efficient algorithms for some of them. |
| File Format | |
| Publisher Date | 2010-01-01 |
| Access Restriction | Open |
| Content Type | Text |
