Abstract. For Markov chains in continuous time Keilson(1979) has shown that the relationship between the ergodic exit time TE and the ergodic sojourn time TV is identical to the relationship between the residual lifetime and the underlying lifetime at ergodicity in renewal theory. Keilson’s result relies upon the memory-less property of exponential distributions, and it would not hold true, in general, for semi-Markov processes. The purpose of this paper is to introduce a new per-formance measure called the ergodic residual exit time TW so as to prove that the relationship between TW and TV for semi-Markov processes is identical to the relationship between TE and TV for Markov chains in continuous time
The discrete and continuous parameter forms of the mean ergodic theorem conclude that as N --> [infi...
International audienceAbstract For continuous-time ergodic Markov processes, the Kemeny time τ ∗ is ...
A semi‐Markov process is a generalization of continuous‐time Markov chain, so that the sojourn times...
For Markov chains in continuous time Keilson(1979) has shown thatthe relationship between the ergodi...
By appealing to renewal theory we determine the equations that the mean exit time of a continuous-ti...
A new construction of regeneration times is exploited to prove ergodic and renewal theorems for semi...
By appealing to renewal theory we determine the equations that the mean exit time of a continuous-ti...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
The following paper, first written in 1974, was never published other than as part of an internal re...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
The relaxation time TREL of a finite ergodic Markov chain in continuous time, i.e., the time to reac...
Abstract For continuous-time ergodic Markov processes, the Kemeny time τ ∗ is the characteristic tim...
discrete-time Markov chains and renewal processes exhibit convergence to stationarity. In the case o...
The discrete and continuous parameter forms of the mean ergodic theorem conclude that as N --> [infi...
International audienceAbstract For continuous-time ergodic Markov processes, the Kemeny time τ ∗ is ...
A semi‐Markov process is a generalization of continuous‐time Markov chain, so that the sojourn times...
For Markov chains in continuous time Keilson(1979) has shown thatthe relationship between the ergodi...
By appealing to renewal theory we determine the equations that the mean exit time of a continuous-ti...
A new construction of regeneration times is exploited to prove ergodic and renewal theorems for semi...
By appealing to renewal theory we determine the equations that the mean exit time of a continuous-ti...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
The following paper, first written in 1974, was never published other than as part of an internal re...
Abstract A semi-Markov process is one that changes states in accordance with a Markov chain but take...
The relaxation time TREL of a finite ergodic Markov chain in continuous time, i.e., the time to reac...
Abstract For continuous-time ergodic Markov processes, the Kemeny time τ ∗ is the characteristic tim...
discrete-time Markov chains and renewal processes exhibit convergence to stationarity. In the case o...
The discrete and continuous parameter forms of the mean ergodic theorem conclude that as N --> [infi...
International audienceAbstract For continuous-time ergodic Markov processes, the Kemeny time τ ∗ is ...
A semi‐Markov process is a generalization of continuous‐time Markov chain, so that the sojourn times...