renewal

Renewal theory is the branch of probability theory that generalizes the Poisson process for arbitrary holding times. Instead of exponentially distributed holding times, a renewal process may have any independent and identically distributed (IID) holding times that have finite mean. A renewal-reward process additionally has a random sequence of rewards incurred at each holding time, which are IID but need not be independent of the holding times.
A renewal process has asymptotic properties analogous to the strong law of large numbers and central limit theorem. The renewal function



m
(
t
)


{\displaystyle m(t)}
(expected number of arrivals) and reward function



g
(
t
)


{\displaystyle g(t)}
(expected reward value) are of key importance in renewal theory. The renewal function satisfies a recursive integral equation, the renewal equation. The key renewal equation gives the limiting value of the convolution of




m
′

(
t
)


{\displaystyle m'(t)}
with a suitable non-negative function. The superposition of renewal processes can be studied as a special case of Markov renewal processes.
Applications include calculating the best strategy for replacing worn-out machinery in a factory and comparing the long-term benefits of different insurance policies. The inspection paradox relates to the fact that observing a renewal interval at time t gives an interval with average value larger than that of an average renewal interval.

You do not have permission to view the full content of this post. Log in or register now.