Non-uniform Random Numbers - Finite Discrete Distributions

Finite Discrete Distributions

For a discrete probability distribution with a finite number n of indices at which the probability mass function f takes non-zero values, the basic sampling algorithm is straightforward. The interval [0, 1) is divided in n intervals [0, f(1)), [f(1), f(1) + f(2)), ... The width of interval i equals the probability f(i). One draws a uniformly distributed pseudo-random number X, and searches for the index i of the corresponding interval. The so determined i will have the distribution f(i).

Formalizing this idea becomes easier by using the cumulative distribution function

It is convenient to set F(0) = 0. The n intervals are then simply [F(0), F(1)), [F(1), F(2)), ..., [F(n − 1), F(n)). The main computational task is then to determine i for which F(i − 1) ≤ X < F(i).

This can be done by different algorithms:

  • Linear search, computational time linear in n.
  • Binary search, computational time goes with log n.
  • Indexed search, also called the cutpoint method.
  • Alias method, computational time is constant, using some pre-computed tables.
  • There are other methods that cost constant time.

Read more about this topic:  Non-uniform Random Numbers

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