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matlabpoisson
Sunhwa
Sunhwa

Reputation: 125

Understanding Non-homogeneous Poisson Process Matlab code

I have found the following Matlab code to simulate a Non-homogeneous Poisson Process 

function x = nonhomopp(intens,T)
% example of generating a 
% nonhomogeneousl poisson process on [0,T] with intensity function intens

x = 0:.1:T;
m = eval([intens 'x']);
m2 = max(m); % generate homogeneouos poisson process
u = rand(1,ceil(1.5*T*m2));
y = cumsum(-(1/m2)*log(u)); %points of homogeneous pp
y = y(y<T); n=length(y); % select those points less than T
m = eval([intens 'y']); % evaluates intensity function
y = y(rand(1,n)<m/m2); % filter out some points
hist(y,10)

% then run
% t = 7 + nonhomopp('100-10*',5)

I am new to Matlab and having trouble understanding how this works. I have read the Mathworks pages on all of these functions and am confused in four places:

1) Why is the function defined as x and then the intervals also called x? Like is this an abuse of notation?

2) How does the square brackets affect eval, 

eval([intens 'x'])

and why is x in single quotations?

3) Why do they use cumsum instead of sum?

4) The given intensity function is \lambda (t) = 100 - 10*(t-7) with 7 \leq t \leq 12 How does t = 7 + nonhomopp('100-10*',5) represent this?

Sorry if this is so much, thank you!

Upvotes: 2

Views: 2208

Answers (2)

AlessioX
AlessioX

Reputation: 3177

  1. the function is not defined as x: x is just the output variable. In Matlab functions are declared as function [output variable(s)] = <function name>(input variables). If the function has only one output, the square brackets can be omitted (like in your case). The brackets around the input arguments are, as instead, mandatory, no matter how many input arguments there are. It is also good practice to end the body of a function with end, just like you do with loops and if/else.
  2. eval works with a string as input and the square brackets apprently are concatenating the string 'intens' with the string 'x'. x is in quotes because, again, eval works with input in string format even if it's referring to variables.
  3. cumsum and sum act differently. sum returns a scalar that is the sum of all the elements of the array whereas cumsum returns another array which contains the cumulative sum. If our array is [1:5], sum([1:5]) will return 15 because it's 1+2+3+4+5. As instead cumsum([1:5]) will return [1 3 6 10 15], where every element of the output array is the sum of the previous elements (itself included) from the input array.
  4. what the command t = 7 + nonhomopp('100-10*',5) returns is simply the value of time t and not the value of lambda, indeed by looking at t the minimum value is 7 and the maximum value is 12. The Poisson distribution itself is returned via the histogram.

Upvotes: 2

Daniel
Daniel

Reputation: 36710

To answer 2). That's a unnecessary complicated piece of code. To understand it, evaluate only the squared brackets and it's content. It results in the string 100-10*x which is then evaluated. Here is a version without eval, using an anonymous function instead. This is how it should have been implemented.

function x = nonhomopp(intens,T)
% example of generating a 
% nonhomogeneousl poisson process on [0,T] with intensity function intens

x = 0:.1:T;
m = intens(x);
m2 = max(m); % generate homogeneouos poisson process
u = rand(1,ceil(1.5*T*m2));
y = cumsum(-(1/m2)*log(u)); %points of homogeneous pp
y = y(y<T); n=length(y); % select those points less than T
m = intens(y); % evaluates intensity function
y = y(rand(1,n)<m/m2); % filter out some points
hist(y,10)

Which can be called like this

t = 7 + honhomopp(@(x)(100-10*x),5)

Upvotes: 3

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