Tag: sampling

Determinantal learning for selecting subsets in wireless networks

We can’t all talk all the time, while still being heard. That’s true for people and it’s true for wireless networks.

Networks need to schedule when their network nodes can transmit and receive data. In other words, a collection or subset of network nodes is chosen to transmit, while another subset is chosen to receive. (In telecommunication language, this is part of what is called the medium access control or MAC layer.)

The classic scheduler (spatial) Aloha involves each node flipping a biased coin with probability \(p\) of heads occurring. If heads comes up, a node can transmit. If not, it must stand ready to receive data. Simple.

But can we do better?

Determinantal scheduling

A few years ago, a couple collaborators and I become interested in this problem. To develop an intelligent (or, at least, not stupid) scheduler, we adapted a machine (or statistical) learning framework and published the results in a conference paper:

  • 2020 – Błaszczyszyn, Brochard, and Keeler, Coverage probability in wireless networks with determinantal scheduling.

The hint is in the title. Using a determinantal scheduler, we derived mathematical expressions for the coverage probability. I discussed that paper in an earlier post. I produced the numerical results with code located here and here.

From fermions to generative model

We used a learning framework based on determinantal point process, which were originally used to model subatomic particles called fermions such as electrons. A key property is that determinantal points, like fermions, repel each other, so this is a random model for diversity, anti-clustering or negative correlations.

Defined with a kernel matrix and determinants, these random models have convenient mathematical properties. For example, you can both simulate them (exactly) and train (or fit) them with maximum likelihood methods. Using this fermion model, Kulesa and Taskar proposed and pioneered a machine learning framework.

When properly trained (or fitted), determinantal models can act as generative artificial intelligence (or Gen AI) models. In some sense, these point processes are special types of Gibbsian point processes, which are defined with a general energy function called a Hamiltonian. In the field of AI and computer science more broadly, there are many energy-based models, some of which are used for generative models such as Boltzmann machines.

Recent work

A few days ago I was curious what was happening in this research area, and by chance, I noticed this recently uploaded preprint:

  • 2025 – Tu, Saha, and Dhillon –  Determinantal Learning for Subset Selection in Wireless Networks.

I feel more work remains to be done in this area.

Further reading

Wireless networks

Our work was inspired by an earlier paper that we did:

    • 2019 – Błaszczyszyn and Keeler, Determinantal thinning of point processes with network learning applications.

In that paper, which I mentioned in an earlier post, we discussed the potential of using determinantal point processes for scheduling network transmission (or selecting subsets). We pointed out that such a model could be trained on optimized network configurations.

The new work by Tu, Saha, and Dhillon is based on previous work, such as this ambitiously-titled paper:

  • 2019 – Saha and Dhillon – Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks.

This work by Saha and Dhillon work was independent of our work on scheduling, but came out around the same time.

Machine learning

I should stress that none of the above work was possible without the pioneering efforts by Kulesza and Taskar in a series of papers, much of which appears in their book:

  • 2012 – Kulesza and Taskar – Determinantal point processes for machine learning, Now Publishers.

You can find a version of the book here.

The Metropolis-Hastings algorithm in C

There’s an old saying, which I just made up. If you really want to understand something, code it up in C.1Taking it to the next level, I recall an undergraduate subject on programming Motorola chips using assembly. But I wouldn’t recommend any of that.

I wrote some C code that basically does what my code in this previous post does without the pretty pictures. The code performs a Metropolis-Hastings algorithm, simulating random variables (or, more correctly, generating random variates) for a joint probability density describing two random variables.

In previous posts, I have covered the topic of Markov chain Monte Carlo (MCMC) methods, particularly the central workhorse, the Metropolis(-Rosenbluth-Rosenbluth-Teller-Teller)-Hastings algorithm.  These methods are frequently used in Bayesian statistics, high-dimensional integration, and optimization. For more details on how they work, I have written a couple posts, starting with this one and ending with this one, where I detail the mechanics of MCMC methods.

Let’s call this code MCMC C code2 Wise man once wrote: C:/DOS C:/DOS/RUN RUN/DOS/RUN.

Code considerations

The C programming language was not written for playing with random numbers. The standard uniform random number generator in C, simply called rand,  is not good for research level random simulations. But it does the job for illustration purposes.

In lieu of this generator, the Mersenne Twister is a popular algorithm for producing such numbers, which is widely recommended and used. There are implementations of this algorithm in the CUDA and MKL libraries for Nvidia GPUs and Intel CPUs; see this CUDA page and this MKL page for details. Check out this PDF file for further details on the CUDA version.

In addition to that, my C code needs to simulate normal (or Gaussian) random variables. For that I wrote my own simple code using the Box-Muller transform, which I covered in a previous post, so the code would be self-contained and less opaque. But in reality, you should always use functions from a pre-written library for generating variates according to a normal or whichever distribution.

Finally, C was never intended as a scientific language, despite its wide use as one. (Historically, that was Fortran’s job, which is still the workhorse for many serious number-crunching institutes, hence why there’s Fortran -ready version of CUDA.) So when handling sets of numbers, such as vectors and matrices, one has to use pointers and malloc more often that not, which can be a tricky. This is the case here, though the use of pointers in this code is relatively simple.

Where are the pretty pictures?

Unfortunately, when number crunching in C, you don’t immediately have access to plotting libraries that are available in scientific programming languages such as Python and Julia.

But you can simply create .csv (or text) files, and then plot them using whichever library you prefer. And if you have gnuplot installed, you can perform simple one-dimensional histograms using one-line commands such as:

gnuplot -e “plot ‘MCMCData_1D.csv’ using 1 bins=20;” -persist

In the above command, the file MCMCData_1D.csv has the random variates (the simulated random variables) stored in a single column.

Code

I only present code for the more complicated two-dimensional case. The code can be found here.

Note that there’s an option in the code of MCMC_1D.c to plot the results using gnuplot, if it’s installed on your machine. I didn’t include code for plotting the results of the 2-D case as gnuplot doesn’t do a 2-D histogram.

Warning: The code uses the standard pseudo-random number generator in C, which is known for being bad. I only used built-in the C generator to keep my code self-contained. For that reason, I also wrote my code for generating Gaussian (or normal) random variables, by using the Box-Muller transform, but in reality one would never do that for research or industry purposes.

/***********************************************************************
 * Runs a simple Metropolis-Hastings (ie MCMC) algorithm to simulate two
 * jointly distributed random variables with probability density
 * p(x,y)=exp(-(x^4+x*y+y^2)/s^2)/consNorm, where s>0 and consNorm is a
 * normalization constant. The probability density function is defined in
 * the function pdf_single.
 *
 * NOTE: In practice, the value of the normalization constant is not needed, as it cancels out in the algorithm.
 *
 * NOTE: This code will *create* a local file (see variable strFilename) to store results. It will *overwrite* that file if it already exists.
 *
 * WARNING: This code uses the default C random number generator, which is known for failing various tests of randomness.
 * Strongly recommended to use another generator for purposes beyond simple illustration.
 *
 * Author: H. Paul Keeler, 2024.
 * Website: hpaulkeeler.com
 * Repository: github.com/hpaulkeeler/posts
 *
 ***********************************************************/

#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include <stdbool.h>
#include <string.h>

const long double pi = 3.14159265358979323846; // constant pi for generating polar coordinates

// helper function declarations; see below for definitions
static double *unirand(double *randValues, unsigned numbRand);                           // generate  uniform random variables on (0,1)
static double *normrand(double *randValues, unsigned numbRand, double mu, double sigma); // generate normal random variables
static double pdf_single(double x, double y, double s);                      // define probability density to be simulated
static double mean_var(double *set_sample, unsigned numbSim, double *varX);              // calculate meana and variance

int main(int argc, char *argv[])
{

    if (argc > 1)
    {
        fprintf(stderr, "This program takes no arguments...\n");
        exit(1);
    }
    else
    {

        char strFilename[] = "MCMCData_2D.csv"; // filename for storing simulated random variates

        // intializes (pseudo)-random number generator
        time_t timeCPU; // use CPU time for seed
        srand((unsigned)time(&timeCPU));
        // srand(42); //to reproduce results

        bool booleWriteData = true; // write data to file
        bool booleStats = true;     // perform simple mean/std stats

        // parameters
        unsigned numbSim = 1e4;   // number of random variables simulated
        unsigned numbSteps = 200; // number of steps for the Markov process
        double sigma = 2;         // standard deviation for normal random steps

        // probability density parameters
        double s = .5; // scale parameter for distribution to be simulated

        // Metropolis-Hastings variables
        // proposal for a new position in the random walk
        double zxRandProposal;      
        double zyRandProposal;      
        double pdfProposal; // density for proposed position
        double pdfCurrent;  // density of current position
        double ratioAccept; // ratio of densities (ie acceptance probability)
        double uRand;       // uniform variable for Bernoulli trial (ie a coin flip)
        // random step (normally distributed)
        double *p_numbNormX = (double *)malloc(1 * sizeof(double));
        double *p_numbNormY = (double *)malloc(1 * sizeof(double));
//positions of the random walk (ie the simualted random variables after numbSteps)
        double *p_xRand = (double *)malloc(numbSim * sizeof(double));
        double *p_yRand = (double *)malloc(numbSim * sizeof(double));

        (void)unirand(p_xRand, numbSim); // random initial values
        (void)unirand(p_yRand, numbSim); // random initial values

        unsigned i, j; // loop varibales
        for (i = 0; i < numbSim; i++)
        {
            // loop through each random walk instance (or random variable to be simulated)

            pdfCurrent = pdf_single(*(p_xRand + i), *(p_yRand + i), s); // current probability density

            for (j = 0; j < numbSteps; j++)
            {
                // loop through each step of the random walk
                (void)normrand(p_numbNormX, 1, 0, sigma);
                (void)normrand(p_numbNormY, 1, 0, sigma);
                // take a(normally distributed) random step in x and y
                zxRandProposal = (*(p_xRand + i)) + (*p_numbNormX);
                zyRandProposal = (*(p_yRand + i)) + (*p_numbNormY);

                pdfProposal = pdf_single(zxRandProposal, zyRandProposal, s); // proposed probability density

                // acceptance rejection step
                (void)unirand(&uRand, 1);
                ratioAccept = pdfProposal / pdfCurrent;
                if (uRand < ratioAccept)
                {
                    // update state of random walk / Markov chain
                    *(p_xRand + i) = zxRandProposal;
                    *(p_yRand + i) = zyRandProposal;
                    pdfCurrent = pdfProposal;
                }
            }
        }

        free(p_numbNormX);
        free(p_numbNormY);

        if (booleStats)
        {

            // initialize statistics variables (for testing results)
            char strVariable[] = "XY";
            double *p_AllRand[] = {p_xRand, p_yRand};
            double meanTemp = 0;
            double varTemp = 0;
            double stdTemp = 0;
            char strTemp='X';
            for (i = 0; i < 2; i++)
            {
                meanTemp = mean_var(p_AllRand[i], numbSim, &varTemp);
                stdTemp = sqrt(varTemp);
                strTemp=strVariable[i];
                printf("The average of the %c random variables is %lf.\n", strTemp, meanTemp);
                printf("The standard deviation of the %c random  variables is %lf.\n", strTemp, stdTemp);
            }
        }

        if (booleWriteData)
        {
            // print to file
            FILE *outputFile;
            outputFile = fopen(strFilename, "w");
            for (i = 0; i < numbSim; i++)
            {
                fprintf(outputFile, "%lf,%lf\n", *(p_xRand + i), *(p_yRand + i)); // output to file
            }
            fclose(outputFile);
            printf("Data printed to file.\n");
        }
        free(p_xRand);
        free(p_yRand);

        return (0);
    }
}

static double pdf_single(double x, double y, double s)
{
    // returns the probability density of a single point (x,y) inside a simulation window defined below
    double pdf_output;

    // non-zero density window parameters
    double xMin = -1;
    double xMax = 1;
    double yMin = -1;
    double yMax = 1;

    if1
    {
        pdf_output = exp(-((pow(x, 4) + x * y + pow(y, 2)) / (s * s)));
    }
    else
    {
        pdf_output = 0;
    }
    return pdf_output;
}

static double *normrand(double *randValues, unsigned numbRand, double mu, double sigma)
{
    // simulate pairs of iid normal variables using Box-Muller transform
    // https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform

    double U1, U2, thetaTemp, rhoTemp, Z1, Z2;
    int i = 0;
    while (i < numbRand)
    {
        // simulate variables in polar coordinates (theta, rho)
        (void)unirand(&U1, 1);
        thetaTemp = 2 * pi * U1; // create uniform theta values
        (void)unirand(&U2, 1);
        rhoTemp = sqrt(-2 * log(U2)); // create Rayleigh rho values

        // change to Cartesian coordinates
        Z1 = rhoTemp * cos(thetaTemp);
        Z1 = sigma * Z1 + mu;
        randValues[i] = Z1; // assign first of random variable pair
        i++;
        if (i < numbRand)
        {
            // if more variables are needed, generate second value of random pair
            Z2 = rhoTemp * sin(thetaTemp);
            Z2 = sigma * Z2 + mu;
            randValues[i] = Z2; // assign second of random variable pair
            i++;
        }
        else
        {
            break;
        }
    }
    return randValues;
}

static double *unirand(double *randValues, unsigned numbRand)
{ // simulate numbRand uniform random variables on the unit interval
  // storing them in randValues which must be allocated by the caller
  // with enough space for numbRand doubles

    for (int i = 0; i < numbRand; i++)
    {
        randValues[i] = (double)rand() / RAND_MAX;
    }
    return randValues;
}

static double mean_var(double *set_sample, unsigned numbSim, double *varX)
{
    // mean and variance of set_sample
    int i;
    // initialize statistics variables (for testing results)
    double meanX = 0;
    double meanXSquared = 0;
    double tempX;
    for (i = 0; i < numbSim; i++)
    {
        tempX = *(set_sample + i);
        meanX += tempX / ((double)numbSim);
        meanXSquared += tempX * tempX / ((double)numbSim);
    }

    *varX = meanXSquared - meanX * meanX;
    return meanX;
}

Further reading

Here’s some information on the rand function and random number generation:

Acknowledg(e)ments

A hat tip to C and CUDA guru Alex Stivala who pointed out a couple of minor issues in my original C code.

  1. x >= xMin) && (x <= xMax) && (y >= yMin) && (y <= yMax [↩]

Quantum-enhanced Markov chain Monte Carlo

The not-so-mathematical journal Nature recently published a paper proposing a new Markov chain Monte Carlo method:

  • 2023 – Layden, Mazzola, Mishmash, Motta, Wocjan, Kim, and Sheldon – Quantum-enhanced Markov chain Monte Carlo.

Appearing earlier as this preprint, the paper’s publication in such a journal is a rare event indeed. This post notes this, as well as the fact that we can already simulate perfectly1For small instances of the model, we can do this directly. For large instances, we can use coupling from the past proposed by Propp and Wilson. the paper’s test model, the Ising or Potts model.2Wilhelm Lenz asked his PhD student Ernst Ising to study the one-dimensional version of the model. Renfrey Potts studied the generalization and presented it in his PhD. But this is a quantum algorithm, which is exciting and explains how it can end up in that journal.

The algorithm

The paper’s proposed algorithm adds a quantum mechanical edge or enhancement to the classic Metropolis-Hastings algorithm.3More accurately, it should be called the Metropolis-Rosenbluth-Rosenbluth-Teller-Teller-Hastings algorithm. As I covered in a recent post, the original algorithm uses a Markov chain defined on some mathematical space. Running it on a traditional or classical computer, at each time step, the algorithm consists of proposing a random jump and then accepting the proposed jump or not. Owing to the magic of Markov chains, in the long run, the algorithm simulates a desired probability distribution.

The new quantum version of the algorithm uses a quantum computer to propose the jump, while still using a classical computer to accept the proposal or not.4In my Metropolis-Hastings post, the classical jumper process, a discrete-time Markov chain, is replaced with a quantum mechanical variant. The quantum jump proposals are driven by a time-independent Hamiltonian, which is a central object in quantum and, in fact, all physics. This leads to a Boltzmann (or Gibbs) probability distribution for the jumping process.

Then, running the quantum part on a quantum computer, the algorithm will hopefully outperform its classical counterpart. The paper nurtures this hope by giving empirical evidence of the algorithm’s convergence speed. The researchers performed the numerical experiments on a 27-qubit quantum processor at IBM using the platform Qiskit.

Quantum is so hot right now

In recent years researchers have been focusing on such algorithms that exploit the strangeness and spookiness of quantum mechanics. You will see more and more quantum versions of algorithms that appear in statistics, machine learning, and related fields, as suggested by this survey paper, which also appeared in Nature.

Quantum lite

Sometimes quantum mechanics only loosely inspires algorithms and models. In this setting, some of my machine learning work uses determinantal point processes. This kernel-based random model draws direct inspiration from the wave function, a standard object in quantum mechanics. Under suitable simplifying conditions, the model describes the locations of particles known as fermions such as electrons and protons. Still, it’s fascinating that a quantum physics model inspired an interesting random object that has found applications in spatial statistics and machine learning.

Simulating Poisson random variables in Fortran

The hint’s in the title. I wrote a simple function in Fortran for simulating (or sampling) Poisson random variables. (More precisely, I should say that the function generates Poisson variates.) I used the simple direct method. This method is based on the exponential inter-arrival times of the Poisson (stochastic) process.

My code should not be used for large Poisson parameter values (larger than, say, 20 or 30), as the code may be too slow. Other methods exist for larger parameter values, which I’ve discussed previously.

I just use the standard Fortran function random_number for generating (pseudo-)random numbers. I am not an expert in Fortran, but my Poisson function seems to work fine. I wrote and ran a simple test that estimates the first and second moments, which should match for Poisson variables.

My Fortran code is very similar to the code that I wrote in C and C#, which is located here. You should be able to run it on this website or similar ones that can compile Fortran (95) code.

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 Poisson parameter values), as well as a rejection method from a 1979 paper by Atkinson.

I wrote the Poisson code using Fortran 95. There are various books and websites on Fortran. The website tutorialspoint.com gives a good introduction to Fortran. You can also edit, compile and run your Fortran code there with its online Fortran editor. I found this short summary a good start. For alternative Fortran code of a Poisson generator, consult the classic book Numerical Recipes, though I believe the book versions only exist for Fortran 77 and Fortran 90.

Code

On this page I’ve only included the code of the functions for generating uniform and Poisson variates. The rest of the code, including the test, is located here.

!Poisson function -- returns a single Poisson random variable
function funPoissonSingle(lambda) result(randPoisson)
real, intent(in) :: lambda !input
real :: exp_lambda !constant for terminating loop
real :: randUni !uniform variable
real :: prodUni !product of uniform variables
integer :: randPoisson !Poisson variable

exp_lambda= exp(-lambda) 

!initialize variables
randPoisson = -1;
prodUni = 1;
do while (prodUni > exp_lambda)
   randUni = funUniformSingle() !generate uniform variable
   prodUni = prodUni * randUni; !update product
   randPoisson = randPoisson + 1 !increase Poisson variable
end do
end function

!Uniform function -- returns a standard uniform random variable
function funUniformSingle() result(randUni)
real randUni;
call random_seed
call random_number(randUni)

end function

Simulating a Poisson point process on a sphere

In this post I’ll describe how to simulate or sample a homogeneous Poisson point process on the surface of a sphere. I have already simulated this point process on a rectangle, triangle disk, and circle.

Of course, by sphere, I mean the everyday object that is the surface of a three-dimensional ball, where this two-dimensional object is often denoted by \(S^2\).  Mathematically, this is a generalization from a Poisson point process on a circle, which is slightly simpler than randomly positioning points on a disk.  I recommend reading those two posts first, as a lot of the material presented here builds off them.

I have not needed such a simulation in my own work, but I imagine there are many reasons why you would want to simulate a Poisson point process on a sphere. As some motivation, we can imagine these points on a sphere representing, say, meteorites or lightning hitting the Earth.

The generating the number of points is not difficult. The trick is positioning the points on the sphere in a uniform way.  As is often the case, there are various ways to do this, and I recommend this post, which lists the main ones.  I will use two methods that I consider the most natural and intuitive ones, namely using spherical coordinates and normal random variables, which is what I did in the post on the circle.

Incidentally, a simple modification allows you to scatter the points uniformly inside the sphere, but you would typically say ball in mathematics, giving a Poisson point process inside a ball; see below for details.

Steps

As always, simulating a Poisson point process requires two steps.

Number of points

The number of points of a Poisson point process on the surface of a sphere of radius \(r>0\) is a Poisson random variable with mean \(\lambda S_2\), where \(S_2=4\pi r^2\) is the surface area of the sphere. (In this post I give some details for simulating or sampling Poisson random variables or, more accurately, variates.)

Locations of points

For any homogeneous Poisson point process, we need to position the points uniformly on the underlying space, which is in this case is the sphere. I will outline two different methods for positioning the points randomly and uniformly on a sphere.

Method 1: Spherical coordinates

The first method is based on spherical coordinates \((\rho, \theta,\phi)\), where the radial coordinate \(\rho\geq 0\), and the angular coordinates \(0 \leq \theta\leq 2\pi\) and \(0\leq \phi \leq \pi\). The change of coordinates gives \(x=\rho\sin(\theta)\cos(\phi)\), \(y=\rho\sin(\theta)\sin(\phi)\), and \(z=\rho\cos(\phi)\).

Now we use Proposition 1.1 in this paper. For each point, we generate two uniform variables \(V\) and \(\Theta\) on the respective intervals \((-1,1)\) and \((0,2\pi)\). Then we place the point with the Cartesian coordinates

$$X =  r  \sqrt{1-V^2} \cos\Theta, $$

$$Y =  r  \sqrt{1-V^2}\sin\Theta, $$

$$ Z=r V. $$

This method places a uniform point on a sphere with a radius \(r\).

How does it work?

I’ll skip the precise details, but give some interpretation of this method. The random variable \(\Phi := \arccos V\) is the \(\phi\)-coordinate of the uniform point, which implies \(\sin \Phi=\sqrt{1-V^2}\), due to basic trigonometric identities.  The area element in polar coordinates is \(dA = \rho^2 \sin\phi d\phi d\theta \), which is constant with respect to \(\theta\). After integrating with respect to \(\phi\),  we see that the random variable \(V=\cos\Phi\) needs to be uniform (instead of \(\Phi\)) to ensure the point is uniformly located on the surface.

Method 2: Normal random variables

For each point, we generate three standard normal or Gaussian random variables, say, \(W_x\), \(W_y\), and \(W_z\), which are independent of each other. (The term standard here means the normal random variables have mean \(\mu =0\) and standard deviation \(\sigma=1\).)  The three random variables are the Cartesian components of the random point. We rescale the components by the Euclidean norm, then multiply by the radius \(r\), giving

$$X=\frac{rW_x}{(W_x^2+W_y^2+W_z^2)^{1/2}},$$

$$Y=\frac{rW_y}{(W_x^2+W_y^2+W_z^2)^{1/2}},$$

$$Z=\frac{rW_z}{(W_x^2+W_y^2+W_z^2)^{1/2}}.$$

These are the Cartesian coordinates of a point uniformly scattered on a  sphere with radius \(r\) and a centre at the origin.

How does it work?

The procedure is somewhat like the Box-Muller transform in reverse. In the post on the circle setting,  I gave an outline of the proof, which I recommend reading. The joint density of the normal random variables is from a multivariate normal distribution with zero correlation. This joint density is constant on the sphere, implying that the angle of the point \((W_x, W_y, W_z)\) will be uniformly distributed.

The vector formed from the normal variables \((W_x, W_y,W_z)\) is a random variable with a chi distribution.  We can see that the vector from the origin to the point \((X,Y,Z)\) has length one, because we rescaled it with the Euclidean norm.

Plotting

Depending on your plotting software, the points may more resemble points on an ellipsoid than a sphere due to the different scaling of the x, y and z axes. To fix this in MATLAB, run the command: axis square. In Python, it’s not straightforward to do this, as it seems to lack an automatic function, so I have skipped it.

Results

I have presented some results produced by code written in MATLAB and Python. The blue points are the Poisson points on the sphere. I have used a surface plot (with clear faces) to illustrate the underling sphere in black.

MATLAB

Python

Note: The aspect ratio in 3-D Python plots tends to squash the sphere slightly, but it is a sphere.

Code

The code for all my posts is located online here. For this post, the code in MATLAB and Python is here.  In Python I used the library mpl_toolkits for doing 3-D plots.

Poisson point process inside the sphere

Perhaps you want to simulate a Poisson point process inside the ball.  There are different ways we can do this, but I will describe just one way, which builds off Method 1 for positioning the points uniformly. (In a later post, I will modify Method 2, giving a way to uniformly position points inside the ball.)

For this simulation method, you need to make two simple modifications to the simulation procedure.

Number of points

The number of points of a Poisson point process inside a sphere of radius \(r>0\) is a Poisson random variable with mean \(\lambda V_3\), where \(V_3=4\pi r^3\) is the volume of the sphere.

Locations of points

We will modify Method 1 as outlined above. To sample the points uniformly in the sphere, you need to generate uniform variables on the unit interval \((0,1)\), take their cubic roots, and then, multiply them by the radius \(r\). (This is akin to the step of taking the square root in the disk setting.) The random variables for the angular coordinates are generated as before.

Further reading

I recommend this blog post, which discusses different methods for randomly placing points on spheres and inside spheres (or, rather, balls) in a uniform manner.  (Embedded in two dimensions, a sphere is a circle and a ball is a disk.)

Our Method 2 for positioning points uniformly, which uses normal variables, comes from the paper:

  • 1959, Muller, A note on a method for generating points uniformly on n-dimensional spheres.

This sampling method relies upon old observations that normal variables are connected to spheres and circles. I also found this post on a similar topic. Perhaps not surprisingly, the above paper is written by the same Muller behind the Box-Muller method for sampling normal random variables.

Update: The connection between the normal distribution and rotational symmetry has been the subject of some recent 3Blue1Brown videos on YouTube.

Here is some sample Python code for creating a 3-D scatter plot.

Simulating Poisson random variables with large means

There’s basically only one method for simulating Poisson variables with a small parameter value. This direct method, as I call it, uses the inter-arrival times of a homogeneous Poisson (stochastic) process, which I covered in a previous post.

But if large parameter, which coincides with its mean, is large, then this method becomes too slow, so you need to use other methods.  In another post, I briefly surveyed these methods and listed different language libraries that use them, ranging from open source projects NumPy and R to industry-level libraries MLK (by Intel) and cuRand (CUDA) by Nvidia.

To simulate large-mean Poisson random variables, I coded up in Python and MATLAB (for now) two of these methods from the two respective papers:

  • 1979, Atkinson, The computer generation of Poisson random variables;
  • 1993, Hörmann, The transformed rejection method for generating Poisson random variable.

You can find my code here.

Again, the code I wrote is only suitable for large parameter values, where the meaning of large depends on the procedure being used, but it’s typically around 30. Consequently, any code for generating Poisson variables should have an if-statement, using the direct method for small parameter values and another method, such as the ones above, for large parameter values.

Update: In another post I presented these two algorithms in C. You can find my code here.

Algorithms

I’ll give some light details of the two methods, which are referred to as PA and PTRS algorithms in the respective papers. I suggest reading the papers for further details.

Both algorithms are (acceptance-)rejection methods. I discuss this general method, first proposed by Neumann, in another post. In context of generating Poisson variables, these methods are, according to the book by Devroye (page 502), known for being relatively simple to implement and fast to execute.

Both algorithms require calculating the log of a factorial or, equivalently, a log of a gamma function, which is done by using approximations, such those of Stirling or Lanczos. This is a common approach in general, as you’ll find that computer functions, such as those in MATLAB or Python (NumPy), that give values for gamma functions and their logs are always based on approximations of the gamma function.  That means you can write your approximation for the log of a factorial or use a pre-existing function.

Algorithm PA  by Atkinson (1979)

The Algorithm PA proposed by Atkinson, among other methods, is a rejection method that uses a logistic distribution as the envelope distribution. (Often such algorithms use a normal distribution as the envelop distribution.)

After writing my code, I noticed that John D. Cook gave pseudo-code of the method in this post and then presented an implementation in C# in this post. Apart from that, I have not seen any implementations of this method.

Algorithm PTRS by Hörmann (1993)

Hörmann refers to the Algorithm PTRS method as a transformed rejection method. It uses the inverse method, which I covered in a previous post, and a rejection method.

I have only seen one implementation of this algorithm. It’s written in C for the Python library NumPy; see the code here.  You’ll notice my code and that C code is very similar, modulus some logarithm identities.

Possible (small) error: I noticed that in that C code, on line 591, the implementation of step 2.0 of the PTRS Algorithm has a strictly less than condition, so it’s \(k <0\), whereas in the original paper (and hence my code),  the condition is \(k\leq 0\). Perhaps this possible error is insignificant, as the procedure is for large-valued Poisson random variables, so the \(k=0\) scenario rarely happens.

Code

I only present here the code in Python, but you can go here to see the code implemented in MATLAB. I also wrote a script that tests if the generated variates (or simulated values) adhere to a Poisson distribution. This test compares the mean and variance (the ratio of which should be equal to one) and a chi-squared test.

Warning: My Python and MATLAB code is only for illustration purposes. In Python (with SciPy), MATLAB, or similar, you would use the pre-existing functions for simulating Poisson random variables.

Algorithm PA  by Atkinson (1979)

# This code generates Poisson variates (or simulates Poisson variables).
# using a method designed for large (>30) Poisson parameter values.
#
# The generation method is Algorithm PA, a type of rejection method, from 
# the paper:
#
# 1979 - Atkinson - "The Computer Generation of Poisson Random Variables"
#
# In practice, you should *always* use the built-in NumPy function
# random.poisson, which (for large Poisson parameter) uses Algorithm PTRS in the
# paper:
#
# 1993 - Hörmann - "The transformed rejection method for generating Poisson
# random variables"
#
# That method is also suggested by Knuth in Volume 2 of his classic
# series "The Art of Computer Programming".
#
# INPUT:
# mu is a single Poisson parameter (or mean) such that mu>=0.
# OUTPUT:
# result_k is a single Poisson variate (that is, an instance of a Poisson random
# variable), which is a non-negative integer.
#
# Author: H. Paul Keeler, 2019.
# Website: hpaulkeeler.com
# Repository: github.com/hpaulkeeler/posts

import numpy as np;  # NumPy package for arrays, random number generation, etc
import scipy 
from getLogFac import getLogFac # type: ignore

def funPoissonLargePA(mu):
    #precalculate some Poisson-parameter-dependent numbers
    c = 0.767 - 3.36/mu;
    beta = np.pi/np.sqrt(3.0*mu);
    alpha = beta*mu;
    k = np.log(c) - mu - np.log(beta);
    log_mu=np.log(mu);

    result_n=-1; #initialize the Poisson random variable (or variate)
    while (result_n<0):
        U = np.random.uniform(0, 1, 1); #generate first uniform variable
        x = (alpha - np.log((1.0 - U)/U))/beta;

        if (x <-.5):
            continue
        else:
            V = np.random.uniform(0, 1, 1); #generate second uniform variable
            n = np.floor(x+.5);
            y = alpha - beta*x;
            #logfac_n=getLogFac(n); 
            #above can be replaced with scipy function: 
            logfac_n=scipy.special.gammaln(n+1)

            #two sides of an inequality condition
            lhs = y + np.log(V/(1.0 + np.exp(y))**2);
            rhs = k + n*log_mu- logfac_n; # NOTE: uses log factorial n

            if (lhs <= rhs):
                result_n=n;
                return result_n;
            else:
                continue;

            #end if-statement

        #end if-statement
    #end while-loop

#end function

Algorithm PTRS by Hörmann (1993)

# This code generates Poisson variates (or simulates Poisson variables).
# using a method designed for large (>10) Poisson parameter values.
#
# The generation method is Algorthm PTRS, a type of rejection method, from
# the paper:
#
# 1993 - Hörmann - "The transformed rejection method for generating Poisson
# random variables"
#
# WARNING: This code is for illustration purposes only.
#
# In practice, you should *always* use the built-in NumPy function
# random.poisson, which (for large Poisson parameter) uses Algorithm PTRS in the
# above paper.
#
# INPUT:
# mu is a single Poisson parameter (or mean) such that mu>=0.
# OUTPUT:
# result_k is a single Poisson variate (that is, an instance of a Poisson random
# variable), which is a non-negative integer.
#
# Author: H. Paul Keeler, 2019.
# Website: hpaulkeeler.com
# Repository: github.com/hpaulkeeler/posts

import numpy as np;  # NumPy package for arrays, random number generation, etc
import scipy 
from getLogFac import getLogFac # type: ignore

def funPoissonLargePTRS(mu):
    #precalculate some Poisson-parameter-dependent numbers
    b = 0.931 + 2.53 * np.sqrt(mu);
    a =  -0.059 + 0.02483 * b;
    vr = 0.9277 - 3.6224 / (b - 2);
    one_over_alpha=1.1239 + 1.1328/(b - 3.4);

    result_n=-1; #initialize the Poisson random variable (or variate)
    #Steps 1 to 3.1 in Algorithm PTRS
    while (result_n<0):
        #generate two uniform variables
        U = np.random.uniform(0, 1, 1); 
        V = np.random.uniform(0, 1, 1); 

        U=U-0.5;
        us = 0.5 -  abs(U);

        n=np.floor((2 * a / us + b) * U + mu + 0.43);

        if (us>=0.07)&(V<=vr):
            result_n = n;
            return result_n;
        #end if-statement

        if (n<=0) |((us < 0.013) & ( V> us)):
            continue
        #end if-statement

        log_mu = np.log(mu);
        #logfac_n=getLogFac(n); 
        #above can be replaced with SciPy's function: 
        logfac_n = scipy.special.gammaln(n+1);

        #two sides of an inequality condition
        lhs = np.log(V * one_over_alpha / (a/us/us + b));
        rhs = -mu + n * log_mu - logfac_n ;# NOTE: uses log factorial n

        if lhs <= rhs:
            result_n = n;
            return result_n;
        else:
            continue
        #end if-statement


    #end while-loop

#end function

Further reading

Algorithm PA was proposed in the paper:

  • 1979, Atkinson, The computer generation of Poisson random variables.

This algorithm is also given in the book Monte Carlo Statistical Methods by Casella and Robert; see Algorithm A.6.

Algorithm PTRS was proposed in the paper:

  • 1993, Hörmann, The transformed rejection method for generating Poisson random variable.

Simulating Matérn hard-core point processes

If you wanted to create a point process with repulsion, a reasonable first attempt would be to build off a Poisson point process by removing points according to some rule to ensure that no two points were within a certain distance of each other. Using this natural idea, Bertril Matérn proposed a family of repulsive point processes called Matérn hard-core point processes.

More specifically, Matérn proposed several points processes, including two types of hard-core point processes now called Type I and Type II. (Matérn proposed a third type, called Type III, but it’s considerably harder to simulate on a computer, as detailed in this article.) These types of hard-core point processes are completely different to the Matérn cluster point process.

As I discussed in a previous post, the Poisson point process may not be adequate for representing point phenomena whose points exhibit large degrees of repulsion or clustering. I already covered the Matérn and Thomas cluster point processes, which show distinct clustering in their configurations. In this post, I’ll cover Matérn hard-core point processes. The Type I point processes is the easier of the two, so I’ll start with that one.

Overview

Simulating Matérn hard-core point processes requires first simulating a homogeneous Poisson point process with an intensity \(\lambda>0\) on some simulation window, such as a rectangle, which is the simulation window I will use here. I have already written about simulating the homogeneous Poisson point processes on a rectangle and a disk, so those posts are good starting points.

Given the Poisson point process, the points then need to be thinned in such a manner to ensure that for each point, there is no other point within some fixed \(r>0\) of the point. This distance \(r>0\) is the radius of the hard core of each point.

I have already covered the point process operation of thinning. But it’s important to note here that in this construction a dependent thinning is being applied. (If I just applied an independent thinning, then the resulting point process will be another Poisson point process with no repulsion between points.)

Edge effects

The main trick behind sampling this point process is that it’s possible for points inside the simulation window to be thinned due to their closeness to points that are located outside the simulation window. In other words, points outside the simulation window can cause points inside the window to be thinned. (I discussed a very similar issue in the posts on the Matérn and Thomas cluster point processes.)

To remove these edge effects, the underlying Poisson point process must be simulated on an extended version of the simulation window. The points are then thinned according to a dependent thinning, which is covered in the next section. Then only the retained points inside the simulation window are kept and the remaining points are ignored. Consequently, the underling Poisson points are simulated on an extended window, but we only see the final points inside the simulation window.

To create the extended simulation window, we add a strip of width \(r\) all around the simulation window. Why? Well, the distance \(r\) is the maximum distance from the simulation window that another point (outside the simulation window) can exist, while still causing points inside the simulation window to be thinned. This means it is impossible for a hypothetical point beyond this distance (outside the extended window) to cause a point inside the simulation window to be thinned.

Dependent thinning rules

Type I

For each point inside the simulation window, check if there are any other points (including those in the extended window) within distance \(r\) of the point. If no, then keep the point. If yes, then remove the point and the points that are within distance \(r\) of the point. The remaining points inside the simulation window form a Matérn Type I point process.

This is a relatively simple thinning rule, which only requires calculating all the inter-point distances. But it is also a very strong thinning rule, meaning that it removes many points. Depending on the Poisson point process intensity \(\lambda\) and core radius \(r\), it is quite possible that all the points are removed, resulting in an empty configuration.

Now we examine the case when the thinning rule is not as strong.

Type II

To create Matérn Type II point process, we assign an independent uniform random variable to each point of the underlying Poisson point process defined on the extended window. In point process terminology, these random variables are called marks, resulting in a marked point process. In the the context of the Matérn Type II point process, these random random marks are usually called ages.

Then for each point in the simulation window, we consider all the points within distance \(r\) of the point. If this point is the youngest (or, equivalently, the oldest) point, then the point is kept. In other words, the point is only kept if its random mark is smaller (or larger) than the random marks of all the other points within distance \(r\) of the point. The remaining points inside the simulation window form a Matérn Type II point process.

Intensity expressions

Using point process and probability theory, one can derive mathematical expressions for the intensities (that is, the average density of points per unit area). These closed-form expressions can then be used to check that the correct number of points are being generated on average over many simulations.

Type I

The intensity of the Type I point process is given by

\[\mu_1=\lambda e^{-\lambda \pi r^2},\]

where \(\lambda \pi r^2\) is simply the area of the core.

Type II

The intensity of the Type II point process is given by

\[\mu_2=\frac{1}{\pi r^2}(1-e^{-\lambda \pi r^2}),\]

which can be written with the intensity of the the Type I point process as

\[\mu_2=\frac{1}{\pi r^2}(1-\frac{\mu_1}{\lambda}).\]

Code

I wrote the sampling code in MATLAB and Python, which are, as usual, very similar to each other. The code, which is is located here, simulates both Type I and II Matérn points processes. It also compares the empirical intensity to the the values given by the mathematical expressions in the previous section.

MATLAB

The MATLAB code is here.

Python

The Python code is here.

Results

I have plotted single realizations of the Matern Type I and II point processes, as well as the underlying Poisson point process in the same window.

MATLAB

Python

Further reading

Matérn hard-core point processes are covered in standard books on the related fields of spatial statistics, point processes and stochastic geometry, such as the following: Spatial Point Patterns: Methodology and Applications with R by Baddeley, Rubak and Turner, on page 140; Statistical Analysis and Modelling of Spatial Point Patterns Statistics by Illian, Penttinen, Stoyan, amd Stoyan, Section 6.5.2, starting on page 388; and; Stochastic Geometry and its Applications by Chiu, Stoyan, Kendall and Mecke, Section 5.4, starting on page 176. The first two books are particularly good for beginners.

The aforementioned book Spatial Point Patterns: Methodology and Applications with R is written by spatial statistics experts Baddeley, Rubak and Turner. It covers the spatial statistics (and point process simulation) R-package spatstat., which has the functions rMaternI and rMaternII for simulating the two point processes respectively.

Simulating Poisson random variables – Survey of methods

In this post I’ll cover the different ways for generating Poisson random variates. (I often just say simulating Poisson variables.) I’ll then list which methods different language libraries use, ranging from open source projects NumPy and R to industry-level libraries MLK (by Intel) and cuRand (CUDA) by Nvidia.

Direct method doesn’t scale well

In the previous post, I discussed how to sample or generate Poisson random variables or, more correctly, variates. I detailed a direct method that uses the fact that a Poisson stochastic process, which is directly related to a Poisson point process, has inter-arrival times that form independent and identically distributed exponential variables.

The direct method is an easy and intuitive sampling method, explaining why it is often used. I implemented the method in MATLAB, Python, C and C#, which can be found here. Later, in another post, I implemented the same Poisson sampling method in Fortran, which is located here.)

As elegant and exact as this simulation method is, it unfortunately decreases in speed as the Poisson parameter \(\lambda\) increases. In a tutorial published in 1983, Brian D. Ripely, a major figure in spatial statistics, says this about the direct method:

This is simple, but has expected time proportional to \(\lambda\). Some of its competitors use rejection methods with the envelope distribution that of the integer part of a continuous random variable, such as logistic, Laplace and normal mixed with exponential distributions.

We recall that acceptance-rejection or rejections methods involve simulating a random object, such as a random variable, by first simulating another random object of the same type that is easier to simulate.  The simulation method then accepts or rejects these random objects based on a certain ratio. The distribution of the simpler random object that is first simulated is called the envelope distribution. Such rejection methods are one way to simulate Poisson variables.

In short, when simulating Poisson variables, the appropriate simulation algorithm should be chosen based on the Poisson parameter. Consequently, the code of most computer functions for generating Poisson variables will have an if-statement, using the direct method for small parameter values and another method for large parameter values. In addition to that, the method for large Poisson parameter values should be both fast but simple to implement.

We now consider the other methods.

Different methods

Over the years there have been different methods proposed for producing Poisson random variates. In the book Non-uniform random variate generation, Luc Devroye groups (in Section X.3 on page 502) the different methods into five categories coupled with his views. These methods are:

  1. Direct methods based on the homogeneous Poisson stochastic process having exponential inter-arrival times. These methods are simple, but the expected time is proportional to the Poisson parameter \(\lambda\).
  2. Inversion methods that search through a table of cumulative Poisson probabilities. Examples include the papers by Fishman (1976) and Atkinson (1979)*.
  3. Methods that use the recursive properties of the Poisson distribution. The paper by Ahrens and Dieter (1974) uses this approach, and its expected time of completion is proportional to \(\log\lambda\).
  4. Acceptance-rejection (or rejection) methods that give relatively fast but simple algorithms. Such methods are proposed in the papers by Atkinson (1979)*, Ahrens and Dieter (1980) and Devroye (1981) or the technical report by Schmeiser and Kachitvichyanukul (1981).
  5. Acceptance-complement methods that uses a normal distribution as the starting distribution, such as the paper by Ahrens and Dieter (1982). This method is fast, but the code is rather long.

*Atkinson had (at least) two papers on generating Poisson variates published in 1979, but I believe Devroye is referring to the first paper, because in the second paper Atkinson compares methods proposed by others.

For the paper titles, see the Further reading section below.

Code

In a separate post, I present implementations in Python and MATLAB of algorithms found respectively in the papers by Atkinson (1979) and Hörmann (1991).  But these are only for illustration purposes, as Python (with SciPy) and MATLAB only have good functions for generating Poisson variables.

Methods implemented in popular libraries

I’ll now state which methods are used in various programming languages and numerical methods. I won’t go into the details how the methods work, just citing the papers instead.

MATLAB

For small \(\lambda\) values, the MATLAB function poissrnd uses the  direct method (based on inter-arrival times) with a while-loop.

For \(\lambda\) values greater than fifteen, I believe that the MATLAB function poissrnd uses Algorithm PG from the 1974 paper by Ahrens and Dieter, which uses the the generation of gamma and binomial random variates.

But to come to this conclusion, I had to do some investigating. You can skip to the next section if you’re not interested, but now I’ll explain my reasoning.

The MATLAB documentation says it uses a method proposed by Ahrens and Dieter, but these two researchers developed a number of methods for generating Poisson variables. The MATLAB code cites Volume 2 of the classic series by Knuth, who says the method is due to Ahrens and Dieter, but he doesn’t give an exact citation in that section of the book. Confusingly, Knuth cites in his book a couple papers by Ahrens and Dieter for generating different random variates. (Knuth later cites a seemingly relevant 1980 paper by Ahrens and Dieter, but that details another method.)

Both the MATLAB code and Knuth cite the book by Devroye. In his book (Exercise 3.5.2), Devroye discusses one method, among others, from a 1974 paper by Ahrens and Dieter. Another hint is given by examining the code of the MATLAB function poissrnd, which reveals that it uses the function randg to generate gamma variables. In the Ahrens and Dieter 1974 paper, their Algorithm PG (for producing Poisson variates) uses gamma random variables, and it’s suggested to use a parameter value of \(7/8\). This is the same parameter used in the MATLAB code and mentioned by Knuth, confirming that this is the right paper by Ahrens and Dieter.

In summary, for large \(\lambda\) the function MATLAB uses Algorithm PG from the 1974 paper by Ahrens and Dieter, whereas for small values it uses the direct method, which they refer to as the multiplication method.

R

In R, the function rpois use an algorithm outlined in the 1982 paper by Ahrens and Dieter. You can view the R source code here. The two cases for \(\lambda\) (or \(\mu\) in the paper) depend on whether \(\lambda\) is greater than ten or not. For small \(\lambda\), the R function rpois does not use the method based on inter-arrival times, but rather an inversion method based on a table of (cumulative) probabilities given by the Poisson probability distribution.

Python (NumPy)

In NumPy, the function numpy.random.poisson generates Poisson variates. The source code for the NumPy library is here, but for the Poisson function the underlying code is actually written in C; see the distributions.c file located here. For small Poisson parameter \(\lambda\), the code uses the direct method; see the function random_poisson_mult in the code.

For Poisson parameter \(\lambda \geq 10\), the comments in the code reveal that it uses a method from a 1993 paper by Hörmann; see Algorithm PTRS on page 43 of the paper. This is a transformation method, which for NumPy is implemented in the C code as the function random_poisson_ptrs. The method, which Hörmann calls the transformed rejection with squeeze, combines inversion and rejection methods.

Octave

Octave is intended to be a GNU clone of MATLAB, so you would suspect it uses the same methods as MATLAB for generating Poisson random variates. But the Octave function poissrnd uses different methods. The code reveals it generates the Poisson variates with a function called prand. It considers different cases depending on the value of the Poisson parameter \(\lambda\) as well as whether a single variable (that is, a scalar) or vector or matrix of Poisson variates are being generated.

In total, the Octave function prand uses five different methods. For two of the methods, the documentation cites methods from the classic book Numerical Recipes in C (the 1992 edition); see next section. To generate a single Poisson variate with Poisson parameter \(\lambda \leq 12\), the Octave function prand uses the direct method based on inter-arrival times.

Numerical Recipes (Fortran, C and C++)

The book Numerical Recipes is a classic by Press, Teukolsky, Vetterling and Flannery on numerical methods. The books comes in different editions reflecting different publication years and computer languages. (In the first two editions of the book, the authors implemented the algorithms respectively in Fortran and C.)

For generating Poisson variates, the book contents seems to have not changed over the editions that I looked at, which covered the programming languages Fortran (77 and 90), C, and C++. The authors cover Poisson generation in Section 7.3 in the Fortran and C editions. In the third edition of Numerical Recipes, they implement their methods in C++ in Section 7.3.12.

For small values of Poisson parameter \(\lambda\), Numerical Recipes uses the direct method. For \(\lambda >12\) values, an acceptance-rejection method is used, which relies upon finding a continuous version of the discrete Poisson probability distribution.

GSL Library (C)

In the GSL library, one can use the function gsl_ran_poisson, which uses the the direct method of exponential times. The code, which can be viewed here, cites simply Knuth (presumably the second volume). But it seems to use the aforementioend Algorithm PG (for producing Poisson variates) from the 1974 paper by Ahrens and Dieter 1974; see the section above on MATLAB.

NAG Library (C)

Although I didn’t see the code, it appears that the function nag_rand_poisson (g05tjc ) in the NAG library also uses the direct method, based on the material in the second volume of series by Knuth. But in a 1979 paper Atkinson says that the NAG library uses a method from the 1974 paper by Ahrens and Dieter.

Boost library Random (C++)

The Boost library Random uses the PTRD algorithm proposed in the 1993 paper by Hörmann to generate Poisson variates; see Algorithm PTRD on page 42 of the paper.  In the same paper appears the PTRS method, which is used by Python (NumPy) (though implemented in C), as mentioned above.

MKL library (C)

In the MKL C library written by Intel, there seems to be three methods in use for generating Poisson variates.

The first function is called VSL_RNG_METHOD_POISSON_PTPE, which does the following for a Poisson distribution with parameter \(\Lambda\):

If Λ ≥ 27, random numbers are generated by PTPE method. Otherwise, a combination of inverse transformation and table lookup methods is used. The PTPE method is a variation of the acceptance/rejection method that uses linear (on the fraction close to the distribution mode) and exponential (at the distribution tails) functions as majorizing functions. To avoid time-consuming acceptance/rejection checks, areas with zero probability of rejection are introduced and a squeezing technique is applied.

This function uses the so-called PTPE method, which is outlined in a 1981 technical report by Schmeiser and Kachitvichyanukul.

The second function is called VSL_RNG_METHOD_POISSON_POISNORM, which does the following :

If Λ < 1, the random numbers are generated by combination of inverse transformation and table lookup methods. Otherwise, they are produced through transformation of the normally distributed random numbers.

The third function is called VSL_RNG_METHOD_POISSONV_POISNORM, which does the following:

If Λ < 0.0625, the random numbers are generated by inverse transformation method. Otherwise, they are produced through transformation of normally distributed random numbers.

cuRAND (C)

Finally, there is the  CUDA Random Number Generation library (cuRAND) developed by Nvidia for their (now ubiquitous) graphical processing units (GPUs).   This C/C++ library has a function for generating Poisson variates. To see the C code, copies of it can be found in various GitHub repositories, such as this one. The cuRAND function curand_poisson uses the direct function for Poisson parameter values  less than 64. For parameters values greater than 4000, it uses a normal approximation (rounded to the nearest integer).

For other values, the function curand_poisson uses a rejection method based on an approximation of the incomplete gamma function; see the function curand_poisson_gammainc. The book by Fishman is cited; see Section 8.16.

Further reading

Books

For various Poisson simulation methods, see the stochastic simulation books:

The book by Gentle (Section 5.2.8) also briefly covers Poisson variables.

Of course, it’s a good idea to look at the citations that the different functions use.

Articles

Here is a list of the papers I mentioned in this post:

  • 1974, Ahrens and Dieter, Computer methods for sampling from gamma, beta, poisson and bionomial distributions;
  • 1976, Fishman, Sampling from the Poisson distribution on a computer;
  • 1979, Atkinson, The computer generation of Poisson random variables;
  • 1979, Atkinson, Recent developments in the computer generation of Poisson random variables;
  • 1980, Ahrens and Dieter, Sampling from binomial and Poisson distributions: a method with bounded computation times;
  • 1980, Devroye, The Computer Generation of Poisson Random Variables;
  • 1981, Schmeiser and Kachitvichyanukul, Poisson Random Variate Generation;
  • 1982, Ahrens and Dieter, Computer generation of Poisson deviates from modified normal distributions;
  • 1983, Ripley, Computer Generation of Random Variables: A Tutorial;
  • 1993, Hörmann, The transformed rejection method for generating Poisson random variable.

Simulating Poisson random variables – Direct method

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 on simulating this point process, such as this one and this one, I’ve simply used the inbuilt functions for simulating (or generating) Poisson random variables (or variates).1In the literature, researchers describe methods for generating 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 Poisson function using just a standard uniform random variate generator? In this post I present my own Poisson simulation code in MATLAB, Python, C and C#, which can be found here.

The 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, then a direct simulation method can be used to generate Poisson random variates. In practice a small Poisson parameter is a number less than some number between 10 to 30.

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 those categories covers the method that I mentioned above. I will cover that method in this post, presenting some Poisson sampling code in C and C#. (I will 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 to use a result based on the homogeneous Poisson stochastic process. The points in time when a homogeneous Poisson stochastic process increases forms a Poisson point process on the real line. 2Confusingly, 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

Here’s the algorithm for sampling Poisson variables with exponential random variables, which I’ll explain.

Sample Poisson random variable \(N\) with parameter (mean) \(\lambda\) using exponential random variables
    1. Set count variable \(N=0\) and initial sum variable \(S=0\);
    2. While \(S<1\):
      1. Sample uniform random variable \(U\sim U(0,1)\);
      2. Calculate \(E= -\log(U)/\lambda \) ;
      3. Update count and sum variables by setting \(N\rightarrow N+1\) and \(S\rightarrow S+E\);
    3. Return N;

The point in time when the Poisson stochastic process increases are called arrival times or occurrence times. In classic random 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
$$ \sum_{k=1}^{n+1} E_k > 1, $$
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 \(1/\lambda\).

But we can skip generating exponential random variates.

Using uniform random variables

Here’s the algorithm for sampling Poisson variables with uniform random variables.

Sample Poisson random variable \(N\) with parameter (mean) \(\lambda\) using uniform random variables
    1. Set count variable \(N=0\) and initial product variable \(P=1\);
    2. While \(P>e^{-\lambda}\):
      1. Sample uniform random variable \(U\sim U(0,1)\);
      2. Update count and product variables by setting \(N\rightarrow N+1\) and \(P\rightarrow P\times U\);
    3. Return N;

To reduce computations, the direct method using exponential random variables is often reformulated as products of uniform random variables. We can do this, due to logarithmic identities, and work with products of uniform variables instead of sums of exponential random variables.

Then, by using standard uniform random variables \(U_1, U_2,\dots\), we define \(N\) to be the smallest \(n\) such that
$$ \prod_{k=1}^{n+1} U_k < e^{-\lambda}. $$ 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

Warning: My online webpage editor tends to mangle symbols like < and >, so it’s best not to copy my code straight from this website, unless you check and edit it, and download my code directly from here.

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. The Poisson point process expert Günter Last studied the origins of this fundamental result. He presented 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 who respectively lived in the 19th and 20th centuries.

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. As always, all my the code is online, with the code from this post being located here.

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 it 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; //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 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; //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 a little change, compared to C, as the C# language is a much more modern language.

Fortran

After this original post, I later wrote a post about implementing the same Poisson algorithm in Fortran. My Fortran code is very similar to the code that I wrote in C and C#. You should be able to run it on this website or similar ones that can compile Fortran (95) code.

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# code in this post.

Simulating Poisson point processes faster

As an experiment, I tried to write code for simulating many realizations of a homogeneous Poisson point process in a very fast fashion. My idea was to simulate all the realizations in two short steps.

In reality, the findings of this experiment and the contents of this post have little practical value, as computers are so fast at generating Poisson point processes. Still, it was an interesting task, which taught me a couple of things. And I did produce faster code.

MATLAB

I first tried this experiment in MATLAB.

Vectorization

In languages like MATLAB, the trick for speed is to use vectorization, which means applying a single operation to an entire vector or matrix (or array) without using an explicit for-loop. Over the years, the people behind MATLAB has advised to use vectorization instead of for-loops, as for-loops considerably slowed down MATLAB code. (But, as as time goes by, it seems using for-loops in MATLAB doesn’t slow the code down as much as it used to.)

Simulating Poisson point processes is particularly amenable to vectorization, due to the independent nature of the point process. One can simulate the number of points in each realization for all realizations in one step. Then all the points across all realizations can also be positioned in one step. In the two-dimensional case, this results in two one-dimensional arrays (or vectors, in MATLAB parlance) for the \(x\) and \(y\) coordinates.  (Of course, in my code, I could have used just one two-dimensional array/vector  for the coordinates of the points, but I didn’t.)

After generating the points, the coordinates of the points need to be grouped into the different realizations and stored in appropriate data structures.

Data structures

The problem with storing point processes is that usually each realization has a different number of points, so more sophisticated data structures than regular arrays are needed. For MATLAB, each point process realization can be stored in a data object called a cell array.  For a structure array, it’s possible for each element (that is, structure) to be a different size, making them ideal for storing objects like point processes with randomly varying sizes.

In the case of two-dimensional point processes, two cell arrays  can be used to store the \(x\) and \(y\) coordinates of all the point process realizations. After randomly positioning all the points, they can be grouped into a cell array, where each cell array element represents a realization of the Poisson point process, by using the inbuilt function MATLAB mat2cell, which converts a matrix (or array) into a cell array.

Alternatively, we could use another MATLAB data object called a structure array. In MATLAB structures have fields, which can be, for example for a point process can be the locations of the points. Given cell arrays of equal size, we can convert them into a single structure array by using the inbuilt MATLAB function struct.

Python

After successfully simulating Poisson point processes in MATLAB, I tried it in Python with the NumPy library.

Vectorization

I basically replicated what I did in MATLB using Python by positioning all the points in a single step. This gives two one-dimensional NumPy arrays for the \(x\) and \(y\) coordinates of all the point process realizations. (Again, I could have instead stored the coordinates as a single two-dimensional array, but I didn’t.)

Perhaps surprisingly, the vectorization step doesn’t speed things up much in Python with the NumPy library. This may be due to the fact that the underlying code is actually written in the C language.  That motivated me to see what methods have been implemented for simulating Poisson variables, which is the topic of the next couple posts.

Data structures

In Python, the data structure  known as a list is the natural choice. Similarly to cell arrays in MATLAB, two lists can be used for the \(x\) and \(y\) coordinates of all the points. Instead of MATLAB’s function mat2cell, I used the NumPy function numpy.split to create two lists from the two NumPy arrays containing the point coordinates.

Python does not seem to have an immediate equivalent to structure arrays in MATLAB. But in Python one can define a new data structure or class with fields, like a structure. Then one can create a list of those data structures with fields, which are called attribute references in Python.

Code

The code in MATLAB and Python can be found here. For a comparison, I also generated the same number of point process realizations (without using vectorization) by using a trusty for-loop. The code compares the times of taken for implemented the two different approaches, labelled internally as Method A and Method B. There is a some time difference in the MATLAB code, but not much of a difference in the Python case.

I have commented out the sections that create data structures (with fields or attribute references) for storing all the point process realizations, but those sections should also work when uncommented.