Bounding Methods for Markov Processes Based on Stochastic Monotonicity and Convexity

확률적 단조성과 콘벡스성을 이용한 마코프 프로세스에서의 범위한정 기법

When {X(t), t ${\geq}$ 0} is a Markov process representing time-varying system states, we develop efficient bounding methods for some time-dependent performance measures. We use the discretization technique for stochastically monotone Markov processes and a combination of discretization and uniformization for Markov processes with the stochastic convexity(concavity) property. Sufficient conditions for stochastic monotonocity and stochastic convexity of a Markov process are also mentioned. A simple example is given to demonstrate the validity of the bounding methods.