Publication details
Computing the Expected Accumulated Reward and Gain for a Subclass of Infinite Markov Chains
| Authors | |
|---|---|
| Year of publication | 2005 |
| Type | Article in Proceedings |
| Conference | 25th International Conference on Foundations of Software Technology and Theoretical Computer Science |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | Infinite Markov Chains; Expected Reward |
| Description | We consider the problem of computing the expected accumulated reward and the average gain per transition in a subclass of Markov chains with countable state spaces where all states are assigned a non-negative reward. We state several abstract conditions that guarantee computability of the above properties up to an arbitrarily small (but non-zero) given error. Finally, we show that our results can be applied to probabilistic lossy channel systems, a well-known model of processes communicating through faulty channels. |
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