Bounding reward measures of Markov models using the Markov decision processes

For a Markov reward process, where upper and lower bounds for the transition rates and rewards are known, a new approach to bound the expected reward is presented. Based on a previous paper where sharp bounds have been defined for the problem, but only an inefficient and unstable algorithm is proposed, this paper presents algorithms to compute the bounds by interpreting the problem as a Markov Decision Process. In this way, the well known value and policy iteration algorithms can be adopted to compute reward bounds in a stable and fairly efficient way. Different numerical techniques are presented for computing the reward bounds. Copyright (C) 2011 John Wiley & Sons, Ltd.