The acceptance(-rejection) method for simulating random variables

In a previous post, I covered a simple but much used method for simulating random variables or, rather, generating random variates. To simulate a random variable, the method requires, in an easy fashion, calculating the inverse of its cumulative distribution function. But you cannot always do that.

In lieu of this, the great John von Neumann wrote in a 1951 paper that you can sample a sequence of values from another probability distribution, accepting only the values that meet a certain condition based on this other distribution and the desired distribution, while rejecting all the others. The accepted values will follow the desired probability distribution. This method of simulation or sampling is called the rejection method, the acceptance method, and it has even the double-barrelled name the acceptance-rejection (AR) method.


Let \(X\) be a continuous random variable with a (probability) density \(p(x)\), which is the derivative of its cumulative probability distribution \(P(X\leq x)\). The density \(p(x)\) corresponds to the desired or target distribution from which we want to sample. For whatever reason, we cannot directly simulate the random variable \(X\). (Maybe we cannot use the inverse method because \(P(X\leq x)\) is too complicated.)

The idea that von Newman had was to assume that we can easily simulate another random variable, say, \(Y\) with the (probability) density \(q(x)\). The density \(q(x)\) corresponds to a proposal distribution that we can sample (by using, for example, the inverse method).

Now we further assume that there exists some finite constant \(M>0\) such that we can bound \(p(x)\) by \(Mq(x)\), meaning

$$ p(x) \leq M q(x), \text{ for all } x . $$

Provided this, we can then sample the random variable \(Y\) and accept a value of it (for a value of \(X\)) with probability

$$\alpha = \frac{p(Y)}{Mq(Y)}.$$

If the sampled value of \(Y\) is not accepted (which happens with probability \(1-\alpha\)), then we must repeat this random experiment until a sampled value of \(Y\) is accepted.


We give the pseudo-code for the acceptance-rejection method suggested by von Neumann.

Random variable \(X\) with density \(p(x)\)

  1. Sample a random variable \(Y\) with density \(q(x)\), giving a sample value \(y\).
  2. Calculate the acceptance probability \(\alpha = \frac{p(y)}{Mq(y)}\).
  3. Sample a uniform random variable \(U\sim U(0,1)\), giving a sample value \(u\).
  4. Return the value \(y\) (for the value of \(X\)) if \(u\leq \alpha\), otherwise go to Step 1 and repeat.

As covered in a previous post, Steps 3 and 4 are equivalent to accepting the value \(y\) with probability \(\alpha\).

Point process application

In the context of point processes, this method is akin to thinning point processes independently. This gives a gives a method for positioning points non-uniformly by first positioning the points uniformly. The points are then thinned based on the desired intensity function. As I covered in a previous post, this is one way to simulate an inhomogeneous (or nonhomogeneous) Poisson point process.


Of course the number of experiment runs (Steps 1 to 3) until acceptance is a geometric variable with parameter \(\alpha\). On average the acceptance(-rejection) method will take \(1/\alpha\) number of simulations to sample one value of the random \(X\) of the target distribution. The key then is to make the proposal density \(q(x)\) as small as possible (and adjust \(M\) accordingly), while still keeping the inequality \(p(x) \leq M q(x)\).

Higher dimensions

The difficulty of the acceptance(-rejection) method is finding a good proposal distribution such that the product \(Mq(x)\) is not much larger than the target density \(p(x)\). In one-dimension, this can be often done, but in higher dimensions this becomes increasingly difficult. Consequently, this method is typically not used in higher dimensions.

Another approach with an acceptance step is the Metropolis-Hastings method, which is the quintessential Markov chain Monte Carlo (MCMC) method. This method and its cousins have become exceedingly popular, as they give ways to simulate collections of dependent random variables that have complicated (joint) distributions.

Further reading

The original paper where the acceptance(-rejection) method appears (on page 769 in the right-hand column) is:

  • von Neumann, Various techniques used in connection with random digits, 1951.

The usual books on stochastic simulations and Monte Carlo methods will detail this method. For example, see the book by Devroye (Section II.3) or the more recent Handbook of Monte Carlo Methods (Section 3.1.5) by Kroese, Taimre and Botev. The book Stochastic Simulation: Algorithms and Analysis by Asmussen and Glynn also covers the method in Section 2b.

Other books include those by Fishman (Section 8.5) and Gentle (Section 4.5) respectively.

The Box-Muller method for simulating normal variables

In the previous post, I covered a simple but much used method for simulating random variables or, rather, generating random variates. To simulate a random variable, the method requires writing down, in a tractable manner, the inverse of its cumulative distribution function.

But in the case of the normal (or Gaussian) distribution, there is no closed-form expression for its cumulative distribution function nor its inverse. This means you cannot, in an elegant and fast way at least, generate with the inverse method a single normal random variable using a single uniform random variable.

Interestingly, however, you can generate two (independent) normal variables with two (independent) uniform variables using the Box-Muller method, originally proposed by George Box and Mervin E. Muller. This approach uses the inverse method, but in practice it’s not used much (see below). I detail this method because I find it neat and it highlights the connection between the normal distribution and rotational symmetry, which has been the subject of some recent 3Blue1Brown videos on YouTube.

(This method was also used to simulate the Thomas point process, which I covered in a previous post.)

Incidentally, this connection is also mentioned in a previous post on simulating a Poisson point process on the surface of a sphere.  In that method post, Method 2 uses an observation by the Muller that normal random variables can be used to position points uniformly on spheres.

I imagine this method was first observed by transforming two normal variables, instead of guessing various distribution pairs that would work.  Then I’ll sketch the proof in the opposite direction, though it works in both directions.

Proof outline

The joint probability density of two independent variables is simply the product of the two individual probabilities densities. Then the joint density of two standard normal variables is


Now it requires a change of coordinates in two dimensions (from Cartesian to polar) using a Jacobian determinant, which in this case is \(|J(\theta,r)=r|\).1Alternatively, you can simply recall the so-called area element \(dA=r\,dr\,d\theta\).  giving a new joint probability density

$$f_{\Theta,R}(\theta,r)=\left[\frac{1}{\sqrt{2\pi}}\right]\left[ r\,e^{-r^2/2}\right]\,.$$

Now we just identify the two probability densities. The first probability density corresponds to a uniform variable on \([0, 2\pi]\), whereas the second is that of a Rayleigh variable with parameter \(\sigma=1\). Of course the proof works in the opposite direction because the transformation (between Cartesian and polar coordinates) is a one-to-one function.


Here’s the Box-Muller method for simulating two (independent) standard normal variables with two (independent) uniform random variables.

Two (independent) standard normal random variable \(Z_1\) and \(Z_2\)

  1. Generate two (independent) uniform random variables \(U_1\sim U(0,1)\) and \(U_2\sim U(0,1)\).
  2. Return \(Z_1=\sqrt{-2\ln U_1}\cos(2\pi U_2)\) and \(Z_2=\sqrt{-2\ln U_1}\sin(2\pi U_2)\).

The method effectively samples a uniform angular variable \(\Theta=2\pi U_2\) on the interval \([0,2\pi]\) and a radial variable \(R=\sqrt{-2\ln U_1}\) with a Rayleigh distribution.

The algorithm produces two independent standard normal variables. Of course, as many of us learn in high school, if \(Z\) is a standard normal variable, then the random variable \(X=\sigma Z +\mu\) is a normal variable with mean \(\mu\) and standard deviation \(\sigma>0\) .

The Box-Muller method is rarely used

Sadly this method isn’t typically used, as historically computer processors were slow at doing the calculations, so other methods were employed such as the ziggurat algorithm. Also, although processors can now do such calculations much faster, many languages, not just scientific ones, come with functions for generating normal variables. Consequently, there’s not much need in implementing this method.

Further reading


Many websites detail this method. Here’s a couple:


The original paper (which is freely available here) is:

  • 1958 – Box and Muller, A Note on the Generation of Random Normal Deviates.

Another paper by Muller connects normal variables and the (surface of a) sphere:

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


Many books on stochastic simulation cover the Box-Muller method. The classic book by Devroye with the descriptive title Non-Uniform Random Variate Generation covers this method; see Section 4.1. There’s also the Handbook of Monte Carlo Methods by Kroese, Taimre and Botev; see Section Ripley also covers the method (and he makes a remark with some snark that many people incorrectly spell it the Box-Müller method); see Section 3.1. The book Stochastic Simulation: Algorithms and Analysis by Asmussen and Glynn also mention the method and a variation by Marsaglia; see Examples 2.11 and 2.12.

The inverse method for simulating random variables

We will cover a simple but much used method for simulating random variables or, rather, random variates. Although the material here is found in introductory probability courses, it frequently forms the foundation of more advance stochastic simulation techniques, such as Markov chain Monte Carlo methods.


The basics of probability theory tell us that any random variable can, in theory, be written as a function of a uniform random variable \(U\) distributed on the interval \((0,1)\), which is usually written as \(U\sim U(0,1)\). All one needs is the inverse of the cumulative distribution function of the desired random variable.

More specifically, let \(X\) be a random variable with a cumulative distribution function \(F(x)=\mathbb{P}(X\leq x)\). The function \(F\) is nondecreasing in \(x\), so its inverse can be defined as \(F^{-1}(y)=\inf\{x:F(x)\geq y\}\), which is known as the generalized inverse of \(F(x)\).

(Some authors assume the minimum is attained so the infimum is replaced with the minimum, giving \(F^{-1}(y)=\min\{x:F(x)\geq y\}\).)

In short, the following result is all that we need.

Transform of a uniform variable \(U\sim U(0,1)\)

For a uniform random variable \(U\sim U(0,1)\), the random variable \(F^{-1}(U)\) has the cumulative distribution function \(\mathbb{P}(F^{-1}(U)\leq x)=P(U\leq F(x))=F(x)\).


The above observation gives a method, which I like to call the direct method, for exactly simulating a random variable \(X\) with the (cumulative) distribution (function) \(F\).

Random variable \(X\) with distribution \(F\)

  1. Sample a uniform random variable \(U\sim U(0,1)\), giving a value \(u\).
  2. Return the value \(x=F^{-1}(u)\) as the sampled value of \(U\).

But this approach only works if we can write down (in a relatively straightforward way) the inverse \(F^{-1}\), which is usually not the case. This means you cannot generate, for example, simulate a normal random variable with a single uniform random variable by using just the inverse method, as we cannot write down the inverse of its cumulative distribution function.

(Interestingly, with two (independent) uniform random variables, we can use the transform method to simulate two (independent) normal (or Gaussian) random variables. This approach is called the Box-Muller transform, which I’ll cover in another post.)

Nevertheless, we can apply the inverse method to some useful distributions.


Warning: The following examples are only for illustration purposes. Except for the Bernoulli example, you would never use them in standard scientific languages such as MATLAB, Python (with NumPy), R or Julia, because those languages already have much better functions for simulating these and many other random variables (or variates). If you are writing a function in a language that lacks such functions, I would consult one of the references mentioned below. Although the inverse method is usually intuitive and elegant, it is often not the fastest method.

Bernoulli distribution

The simplest random variable is that with the Bernoulli distribution. With probability \(p\), a Bernoulli random variable \(X\) takes the value one. Otherwise, \(X\) takes the value zero (with probability \(1-p\)). This gives the (cumulative) distribution (function):

$$ F_B(x)=\begin{cases}
0 & \text{if } x < 0 \\
1 – p & \text{if } 0 \leq x < 1 \\
1 & \text{if } x \geq 1

This gives a very simple way to simulate (or sample) a Bernoulli variable \(X\) with parameter \(p\).

Bernoulli random variable \(X\) with parameter \(p\)

  1. Sample a uniform random variable \(U\sim U(0,1)\), giving a value \(u\).
  2. If \(u\leq p\), return \(x=1\); otherwise return \(x=0\).
Application: Acceptance simulation methods

In random simulation code, whenever you do something (or not) with some probability \(p\) (or probability \(1-p\)), then the code will perform the above step. Consequently, you see this in the (pseudo-)code of many stochastic simulations with random binary choices, particularly schemes that have an acceptance step such the Metropolis-Hastings method and other Markov chain Monte Carlo (MCMC) methods.

In MCMC schemes, a random (binary) choice is proposed and it is accepted with a certain probability, say, \(\alpha \). This is the equivalent of accepting the proposed choice if some uniform random variable \(U\) meets the condition \(U\leq \alpha\).

This explains why the pseudo-code of the same algorithm can vary. Some pseudo-code will say accept with probability \(\alpha\), while other pseudo-code will say do if \(U\leq \alpha\). It’s two equivalent formulations.

Exponential distribution

The cumulative distribution function of an exponential variable with mean \(1/\lambda\) is \(F_E(x)= 1-e^{-\lambda x}\), which has the inverse \(F^{-1}_E(y)=-(1/\mu)\ln[1-y]\). We can use the fact that on the interval \((0,1)\), a uniform variable \(U\sim U(0,1)\) and \(1-U\) have the same distribution. Consequently, the random variables \(\ln [1-U]\) and \(\ln U\) are equal in distribution.

This gives a method for simulating exponential random variables.

Exponential random variable \(X\) with mean \(1/\lambda\)

  1. Sample a uniform random variable \(U\sim U(0,1)\), giving a value \(u\).
  2. Return \(x=-(1/\lambda)\ln u\).
Application: Poisson simulation method

Of course you can use this method to simulate exponential random variables, but it has another application. In a previous post on simulating Poisson variables, I mentioned that exponential random variables can be used to simulate a Poisson random variable in a direct (or exact) manner. That method is based on the distances between the points of a homogeneous Poisson point process (on the real line) being exponential random variables.

But this method is only suitable for low values of \(\lambda\), less than say fifteen.

Rayleigh distribution

The Rayleigh distribution is \(\mathbb{P}(X<x)= (x/\sigma^2)e^{-x^2/(2\sigma^2)}\), where \(\sigma>0\) is its scale parameter. The square root of an exponential variable with mean \(1/\lambda\) has a Rayleigh distribution with scale parameter \(\sigma=1/\sqrt{2\lambda}\).

Consequently, the generation method is similar to the previous example.

Rayleigh random variable \(Y\) with scale parameter \(\sigma>0\)

  1. Sample a uniform random variable \(U\sim U(0,1)\), giving a value \(u\).
  2. Return \(y=\sigma\sqrt{-2\ln u}\).

Other methods

The inverse method is intuitive and often succinct. But most functions for simulating random variables (or, more correctly, generating random variates) do not use these methods, as they are not fast under certain parameter regimes, such as large means. Consequently, other method are used such as approximations (with, say, normal random variables), such as the ones I outlined in this post on simulating Poisson random variables.

More complicated random systems, such as collections of dependent variables, can be simulated using Markov chain Monte Carlo methods, which is the direction we’ll take in a couple posts after this one.

Further reading

The inverse technique is in your favourite introductory book on probability theory. The specific examples here are covered in books on stochastic simulations and Monte Carlo methods. The classic book by Devroye covers these topics; see Section 2.1 and the examples (inverse method) in Chapter 2.

For a modern take, there’s the extensive Handbook of Monte Carlo Methods by Kroese, Taimre and Botev; see Section 3.1.1, Algorithm 4.1 (Bernoulli) and Algorithm 4.29 (exponential), and Algorithm 4.66 (Rayleigh). There’s also the book Stochastic Simulation: Algorithms and Analysis by Asmussen and Gynn; in Chapter 2, see Example 2.1 (Bernoulli) and Exampe 2.3 (exponential).

Other books include those by Fishman (Section 8.1) and Gentle (Section 4.1) respectively. (Warning: the book by Gentle has a mistake on page 105 in algorithm for sampling Bernoulli variables, as noted by the author. It should be \(1-\pi\) and not \(\pi\) when zero is returned for the sampled value of the Bernoulli variable.)