If you were to write from scratch a program that simulates a homogeneous Poisson point process, the trickiest part would be the random number of points, which requires simulating a Poisson random variable. In previous posts, here and here, I’ve simply used the inbuilt functions for simulating (or generating) Poisson random variables (or variates). (In the literature, researchers describe methods on how to generate random *deviates *or variates. But, in my informal way, I will often say *simulate* *random variables* or *generate* *random **variates,* somewhat interchangeably.)

But how would one create such a function using just a standard uniform random variate generator?

The generator or method being used depends on the value of the Poisson parameter, denoted here by \(\lambda\), which is the mean (as well as the variance) of a random variable with a Poisson distribution. If this parameter value is small, which in practice has been less than some number between 10 to 30, then a direct simulation method can be used to generate Poisson random variates.

For large \(\lambda\) values, other methods are generally used, such as rejection or (highly accurate) approximation methods. In the book *Non-uniform random variate generation**, *the author Luc Devroye groups the methods into five categories (Section X.3.2), which I briefly describe in the next post. The first of these categories covers the method that I mentioned above, which is what I’ll cover in this post, with some code in C and C#. (I also present some code in MATLAB, but you would never use it instead of the the inbuilt function poissrnd.)

In the next post, I’ll describe other types of Poisson simulation methods, and I’ll detail which simulation methods various programming libraries use.

### Direct method

An elegant and natural method for simulating Poisson variates is using a result based on the homogeneous Poisson *stochastic* process. The points in time when a given homogeneous *Poisson stochastic process* increases forms a *Poisson point process* on the real line. Confusingly, the term *Poisson process* is often used to refer to both the stochastic process and the point process, but there is a slight difference.

##### Using exponential random variables

The times when the Poisson stochastic process increases are called *arrival times* or *occurrence times*, as in classic stochastic models they represent the arrivals or occurrences of something, such as phone calls over time. The differences between consecutive times are called *inter-arrival times* or *inter-occurrence* times. The inter-arrival times of a homogeneous Poisson process form independent *exponential* random variables, a result known as the *Interval Theorem*.

Using this connection to the Poisson stochastic process, we can generate exponential variables \(E_1\), \(E_2, \dots \), and add them up. The smallest number of exponential variables for the resulting sum to exceeds one will give a Poisson random variable. That is, if we define \(N\) to be the smallest \(n\) such that

$$ \sum_{k=1}^n E_k > 1, $$

then \(N\) is a random variable distributed according to a Poisson distribution.

Generating exponential variates is easily done by using the inverse method. For a uniform random variable \(U\) on the unit interval \((0,1)\), the transformation \(E= -\log(U)/\lambda \) gives an exponential random variable with mean \(1/\lambda\).

##### Using uniform random variables

To reduce computation overhead, the direct method using exponential random variables is often reformulated as products of uniform random variables, due to logarithmic identities, instead of sums of exponential random variables. Then using standard uniform random variables \(U_1, U_2,\dots\), we define \(N\) to be the smallest \(n\) such that

$$ \prod_{k=1}^n U_k > e^{-\lambda}. $$

These two different formulations of the same method are captured by Lemma 3.2 and Lemma 3.3 in Chapter 10 of Devroye’s book.

##### Example in MATLAB

In MATLAB, we can implement this method with the first formulation in a function with a simple while-loop:

function N=funPoissonLoop(lambda) T=0; %initialize sum of exponential variables as zero n=-1;%initialize counting variable as negative one while (T < 1) E=-(1/lambda)*log(rand(1));%generate exponential random variable T=T+E; %update sum of exponential variables n=n+1; %update number of exponential variables end N=n; end

But, as I said before, don’t use this code instead of the inbuilt function poissrnd.

If you want to be a bit more tricky, you could achieve the same result by using recursion:

function N=funPoissonRecursive(lambda) T=0; %initialize sum of exponential variables as zero n=-1; %initialize counting variable as negative one %run (recursive) exponential function step function [~,N]=funStepExp(lambda,T,n); function [T,N]=funStepExp(nu,S,m) if (S < 1) %run if sum of exponential variables is not high enough %generate exponential random variable E=(-log(rand(1)))/nu; S=S+E; %update sum of exponential variables m=m+1; %update nunber of exponential variables %recursively call function again [T,N]=funStepExp(nu,S,m); else T=S; N=m; end end end

Note how the code recursively calls the function *funStepExp*, which generates an exponential variable each time.

In the Code section below I describe my code in C and C#, using the second formulation.

### Origins

Some people attribute the direct method, based on inter-arrival times, to (or, at least, cite) Donald Knuth, who details it in the second volume of his classic series of books, but I doubt that the great Knuth was the first to have this idea. For example, a quick search on Google Scholar found a paper by K. D. Tocher on computers and random sampling, where Tocher proposes the direct method in 1954, some years before Knuth started publishing his classic series.

The direct method for Poisson sampling relies upon the Interval theorem. Günter Last studied its history in a recent book authored by him and Matthew Penrose; see Chapter 7 and its corresponding historical footnotes in Section C of the appendix. (A free version of the book can be found here. ) People connected to the result include Robert Ellis and William Feller.

### Other methods

The direct method perfectly generates Poisson random variables (or I should say *Poisson random variates*). But it can be too slow for large values of the Poisson parameter (that, is the mean) \(\lambda\). This has motivated researchers to develop other methods, which I will mention in the next post.

### Code

I wrote some code that simulates Poisson random variables by employing the direct method based on exponential inter-arrival times. I have implemented the second formulation (using just uniform variables) in the C and C# languages. In the code, I have used a while-loop to implement the method, but I could have also used a recursion method, as I did in the MATLAB example above, which I have also done in Python (with NumPy).

For an empirical test, the code also calculates the mean and variance of a collection of Poisson variables. For a large enough number of variables, the sample mean and the variance will closely agree with each other, converging to the same value.

##### C

**Warning**: My C code uses rand(), the standard pseudo-random number function in C, which is known for failing certain tests of randomness. The function is adequate for regular simulation work, but gives poor results for large number of simulations. Replace this function with another pseudo-random number generator.

The code for generating a single Poisson variate is fairly straightforward in C. Here’s a sample of the code with just the Poisson function:

//Poisson function -- returns a single Poisson random variable int funPoissonSingle(double lambda) { double exp_lambda = exp(-lambda); //constant for terminating loop double randUni; //uniform variable double prodUni; //product of uniform variables int randPoisson = -1; //Poisson variable //initialize variables randPoisson = -1; prodUni = 1; do { randUni = funUniformSingle(); //generate uniform variable prodUni = prodUni * randUni; //update product randPoisson++; //increase Poisson variable } while (prodUni > exp_lambda); return randPoisson; }

For generating multiple variates, the code becomes more complicated, as one needs to use pointers, due to the limited or static memory capabilities of C. Again, the function uses the pseudo-random number generator in C.

##### C#

The code for generating a single Poisson variate is also straightforward in C#. Here’s the function in C#:

//Poisson function -- returns a single Poisson random variable public int funPoissonSingle (double lambda) { double exp_lambda = Math.Exp (-lambda); //constant for terminating loop double randUni; //uniform variable double prodUni; //product of uniform variables int randPoisson = -1; //Poisson variable //initialize variables randPoisson = -1; prodUni = 1; do { randUni = funUniformSingle (); //generate uniform variable prodUni = prodUni * randUni; //update product randPoisson++; // increase Poisson variable } while (prodUni > exp_lambda); return randPoisson; }

Generalizing the code so it generates multiple variates just requires little change, compared to C, as the C# language is a much more modern language.

## Further reading

For various Poisson simulation methods, see the stochastic simulation books by Devroye (Section X.3) or Fishman (Section 8.16). There’s a free online version of Devroye’s book here. The book by Gentle (Section 5.2.8) also briefly covers Poisson variables.

In this post on generating Poisson variates, John D. Cook briefly discusses the direct method for small \(\lambda\) values and a rejection method from a 1979 paper by Atkinson, which I will mention in the next post. He presents his C# sharp code in this post.