This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Poisson Process: Special Case of Many Things It is useful to be aware that a Poisson process is a special case of several important stochastic processes. That leads to different equivalent definitions of a Poisson process, as in Definitions 5.2 and 5.3 of the Ross text. It also leads to different ways to analyze a Poisson process. 1. A Point Process and a Counting Process A point process on the positive half line, i.e., on the interval [0 , ∞ ), is a random distribu tion of points on the positive half line. We may specify the distribution in three ways: (i) by specifying the distribution of the locations of the points, (ii) by specifying the distribution of the intervals between successive points and (iii) by specifying the distribution of the associated counting process. Let S n be the location of the n th point, where S ≡ 0 (without there being a th point). Let X n ≡ S n S n 1 be the interval between the ( n 1) st point and the n th point. Let the associated counting process be defined by N ( t ) ≡ max { k ≥ 0 : S k ≤ t } , t ≥ . In other words, a point process may be specified in three ways, via the stochastic processes: (i) { S n : n ≥ } , (ii) { X n : n ≥ 1 } and (iii) { N ( t ) : t ≥ } . The first representation { S n : n ≥ } is the typical form for a point process . The last representation { N ( t ) : t ≥ } is the typical form for a counting process . A picture makes this clear; see Figure 1....
View
Full
Document
This note was uploaded on 01/16/2012 for the course IEOR 4106 taught by Professor Whitward during the Spring '11 term at Columbia College.
 Spring '11
 WhitWard

Click to edit the document details