Moments analysis in Markov reward models
François Castella, Guillaume Dujardin, Bruno Séricola · HAL (Le Centre pour la Communication Scientifique Directe) · 2007
We analyze the moments of the accumulated reward over the interval (0, t) in a continuous-time Markov chain. We develop a numerical procedure to efficiently compute the normalized moments using the uniformization technique. Our algorithm involves auxiliary quantities whose convergence is analyzed, and for which we provide a probabilistic interpretation.