Define and compose random variables




Define and compose random variables.

Random numbers

First, we need a way to generate random numbers. Here, a random number generator is defined dynamically as an object having a method that returns a uniform number in [0,1]:

type Random = object
  random: proc(): float

One can obtain instances of Random by wrapping the RNG defined in nim-random, such as in

import random/urandom, random/mersenne
import alea

var rng = wrap(initMersenneTwister(urandom(16)))

The reason why we need to wrap them is that random number generators in nim-random are defined as a concept, while it will be simpler in the sequel to represent them as a single type.

Random variables

A random variable of type A is just something that can take a random number generator and provide an instance of type A:

type RandomVar[A] = concept x
  var rng: Random
  rng.sample(x) is A

In other word, the only operation that we need to define on a type T to make it an instance of RandomVar[A] is

proc sample(rng: var Random, t: T): A = ...

Here we require the first parameter to be of type var Random because drawing a random number mutates the internal state of the RNG. It may be more clear to return a new state together with the value of type A, much like in the state monad but we avoid doing so for performance reason.

If we think of the internal state space of the random number generator as the probability space Ω, the similarity between our definition and the mathematical definition of random variable is apparent.

A few core random variables are defined:

  • ConstantVar[A] is a just a trivial random variable that always samples the same value
  • Uniform is a uniform variable over a real interval
  • Choice[A] is a discrete random variable that can take a finite number of values with equal probability
  • ClosureVar[A] is a wrapper over a proc(rng: var Random): A

Most random variables that arise by manipulating other variables are of ClosureVar type.

Here is an example showing how to costruct instances of these variables. Types are inferred, and are there just for explanatory purposes:

import alea

proc f(rng: var Random): float = 2 * rng.random()

  c: ConstantVar[string] = constant("hello")
  u: Uniform = uniform(2, 14)
  d: Choice[int] = choice(@[1, 2, 3, 4, 5])
  x: ClosureVar[float] = closure(f)

Operations on random variables

A few common operations on random variables are supported - in particular mapping and filtering:

import alea, future

  a = uniform(3, 12)
  b = float) => 3 - x)
  c = a.filter((x: float) => x > 5)

Mapping, in particular, is a common operation, and there is a macro lift that takes a function A => B and declares a function of the same name of type RandomVar[A] => ClosureVar[B] that is obtained by mapping. It can be used with a type hint in case the function is overloaded, as in

import math

proc sq(x: float): float = x * x

lift(abs, float)
lift(sq) # No ambiguity here

  a = uniform(3, 12)
  b = sq(abs(a))

There is also a version of two arguments map2, that takes two random variables and a binary function. Generalization for more than two arguments can be done using the fact that random variables form a monad but the relevant functions are still to be implemented.

Many mathematical functions, as well as the arithmetic operations, are already lifted, so the following is valid:

  a = uniform(3, 12)
  b = choice(@[1.0, 2.5, 3.7])
  c = abs(a - b) * sqrt(a)

Conditioning random variables

Random variables can also be conditioned with respect to each other. For instance, if a and b are real random variables and we want to condition a to the occurrence that b is positive, we can do:

let c = a.where(b, (x: float) => x > 0)

where the last parameter to where is a predicate that should be satisfied by samples from b.

How to make this work? Drawing from b will change the status of the random number generator, which in theory prevents us from sampling a at the same point.

To avoid this issue, we use a fake random number generator that wraps another instance of Random, but repeats its result twice. That is, internally we use an auxiliary (fake) RNG defined like

var repeated = rng.repeat(2)

You can use the same trick whenever there is the need to draw more than a single sample from the same point of the probability space.

Statistics on random variables

A few common statistics are implemented on RandomVar[float], such as the mean, variance and so on. There is a generic implementation that will work for any random variable, but particular types of random variables can use more specialized methods.

An example of their usage is:

let x = uniform(2, 5) + choice(@[1.2, 3.3, 4.5])
var rng = ...

echo rng.sample(x)
echo rng.mean(x)
echo rng.variance(x)
echo rng.stddev(x)

The covariance is also implemented, again by using the trick of a repeating random number generator to draw from the two distributions at the same time.

All there operations admit an optional parameter which is the number of samples to compute the statistics with more or less accuracy:

echo rng.mean(x, samples = 1000000)

Finally, complex random variables, that are represented by chains of closures, can be approximated by sampling enough times. There is a function discretize that will take any RandomVar[A] and produce an instance of Choice[A] that will wrap a certain number of samples:

let f = ... # Some complex random variable
let d = f.discretize(samples = 20000)

More distributions

A few common real random variables are implemented:

  g = gaussian(mu = 0, sigma = 1)
  p = poisson(3.5)
  b = bernoulli(0.7)

Usually, the statistics for these notable random variables are known, so we have overloads, in such a way that, for instance, the mean of a Gaussian variable will always be exact.

Defining custom distributions

To define your own random variables, you can take inspiration, say, from bernoulli.nim. The only mandatory operation for a type T to be an instance of RandomVar[A] is

proc sample(rng: var Random, t: T): A

If other statistics are known (such as mean, variance and so on), one can also define overloads such as

proc mean(rng: var Random, t: T, samples = 100000)

A complete example

Here is a small example that combines all of the above:

import future
import random/urandom, random/mersenne
import alea

var rng = wrap(initMersenneTwister(urandom(16)))
  a = uniform(0, 9)
  b = choice([1, 2, 3, 4, 5]).map((x: int) => x.float)
  c = poisson(13)
  d = gaussian(mu = 3, sigma = 5).filter((x: float) => x > 3)
  s = ln(abs((sqrt(a) * b) - (a.floor / log10(c)))) + d
  t = c.where(s, (x: float) => x > 5)
  u = rng.discretize(t)

echo rng.mean(s)
echo rng.stddev(u)


  • improve the DSL for conditioning
  • higher moments and other statistics
  • monad composition
  • histograms
  • add more standard distributions (beta, gamma, geometric...)
  • entropy etc.
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