Markov reward model
In probability theory, a Markov reward process is a stochastic process which extends either a Markov chain or continuous-time Markov chain by adding a reward rate to each state. An additional variable records the reward accumulated up to the current time.[1] Features of interest in the model include expected reward at a given time and expected time to accumulate a given reward.[2] The model appears in Ronald A. Howard's book.[3]
Markov chain
Continuous-time Markov chain
The accumulated reward can be computed numerically over the time domain or by evaluating the linear hyperbolic system of equations which describe the accumulated reward using transform methods or finite difference methods.[4]
References
- ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1007/978-1-4615-1387-2_2, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with
|doi=10.1007/978-1-4615-1387-2_2instead. - ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1007/978-3-642-11492-2_10, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with
|doi=10.1007/978-3-642-11492-2_10instead. - ^ Howard, R.A. (1971). Dynamic Probabilistic Systems, Vol II: Semi-Markov and Deccision Processes. New York: Wiley. ISBN 0471416657.
- ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1016/0377-2217(89)90335-4, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with
|doi=10.1016/0377-2217(89)90335-4instead.
 | This probability-related article is a stub. You can help Wikipedia by expanding it. |