Poisson Processes
description
Transcript of Poisson Processes
Poisson ProcessesShane G. Henderson
http://people.orie.cornell.edu/~shane
Shane G. Henderson
A Traditional Definition
2/15
Shane G. Henderson
What Are They For?
3/15
Times of customer arrivals (no scheduling and no groups)
Locations, e.g., flaws on wafers, ambulance call locations,submarine locations
Ambulance call times and locations (3-D)
Shane G. Henderson
“Palm-Khintchine Theorem”
User 1 ★ ★
User 2 ★ ★
User 3 ★
User 4 ★ ★
…
User n ★ ★
4/15
time
Shane G. Henderson
A Point-Process Definition
5/15
Shane G. Henderson
Poisson Point-Processes
6/15
Shane G. Henderson
Superposition
7/15
Shane G. Henderson
Transformations
8/15
Shane G. Henderson
Inversion
9/15
t
Shane G. Henderson
Marking
10/15
t
This “works” because oforder-statistic property
Shane G. Henderson
Thinning
11/15
t
i.i.d.U(0,1)
Thinned points and retained points are in different regions,therefore independent“t” coordinates of retained points are a Poisson process, rate “t” coordinates of thinned points are too, rate
Shane G. Henderson
More on Marking
• Suppose call times for an ambulance follow a Poisson process in time
• Mark each call with the call location (latitude, longitude)
• Resulting 3-D points are those of a Poisson process
12/15
Shane G. Henderson
More on Marking
To generate Poisson processes in > 1 dimension, one way is to
– First generate their projection onto a lower dimensional structure (Poisson)
– Independently mark each point with the appropriate conditional distribution
Saltzman, Drew, Leemis, H. (2012). Simulating multivariate non-homogeneous Poisson processes using projections. TOMACS
13/15
Shane G. Henderson
This View of Poisson Processes
• Is mathematically elegant• Is highly visual and therefore intuitive• Makes proving many results almost as
easy as falling off a log– Try proving thinned and retained points are
independent Poisson processes• Suggests other generation algorithms
14/15