EE226a - Summary of Lecture 28 Review: Part 2 - Markov Chains, Poisson Process, and Renewal Process
نویسنده
چکیده
A deeper observation is that a Markov chain X starts afresh from its value at some random times called stopping times. Generally, a stopping time is a random time τ that is non-anticipative. That is, we can tell that τ ≤ n from {X0, X1, . . . , Xn}, for any n ≥ 0. A simple example is the first hitting time TA of a set A ⊂ X . Another simple example is TA + 5. A simple counterexample is TA − 1. This property is the strong Markov property. It is that property that enables us to say that the successive hitting times of a given state form a renewal process. We have
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