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Generate a random number generator for beta deviates

MuPAD® notebooks are not recommended. Use MATLAB® live scripts instead.

MATLAB live scripts support most MuPAD functionality, though there are some differences. For more information, see Convert MuPAD Notebooks to MATLAB Live Scripts.


stats::betaRandom(a, b, <Seed = n>)


stats::betaRandom(a, b) returns a procedure that produces beta deviates (random numbers) with shape parameters a > 0, b > 0.

The procedure f := stats::betaRandom(a, b) can be called in the form f(). The return value of f() is either a floating-point number or a symbolic expression:

  • If a and b can be converted to positive floating-point numbers, then f() returns a random floating-point number between 0.0 and 1.0.

  • In all other cases, f() return the symbolic call stats::betaRandom(a, b)().

Numerical values of a and b are only accepted if they are positive.

The values X = f() are distributed randomly according to the beta distribution with parameters a and b. For any 0 ≤ x ≤ 1, the probability that Xx is given by


Without the option Seed = n, an initial seed is chosen internally. This initial seed is set to a default value when MuPAD® is started. Thus, each time MuPAD is started or re-initialized with the reset function, random generators produce the same sequences of numbers.


In contrast to the function random, the generators produced by stats::betaRandom do not react to the environment variable SEED.

For efficiency, it is recommended to produce sequences of K random numbers via

f := stats::betaRandom(a, b): f() $ k = 1..K;
rather than by
stats::betaRandom(a, b)() $ k = 1..K;
The latter call produces a sequence of generators each of which is called once. Also note that
stats::betaRandom(a, b, Seed = n)() $ k = 1..K;
does not produce a random sequence, because a sequence of freshly initialized generators would be created each of them producing the same number.

Environment Interactions

The function is sensitive to the environment variable DIGITS which determines the numerical working precision.


Example 1

We generate beta deviates with parameters a = 2 and :

f := stats::betaRandom(2, 3/4): f() $ k = 1..4

delete f:

Example 2

With symbolic parameters, no random floating-point numbers can be produced:

f := stats::betaRandom(a, b): f()

When a and b evaluate to positive real numbers, the generator starts to produce random numbers:

a := 1: b := 2: f() $ k = 1..4

delete f, a, b:

Example 3

We use the option Seed = s to reproduce a sequence of random numbers:

f := stats::betaRandom(1, 3, Seed = 1): f() $ k = 1..4

g := stats::betaRandom(1, 3, Seed = 1): g() $ k = 1..4

f() = g(), f() = g()

delete f, g:


a, b

The shape parameters of the beta distribution: arithmetical expressions representing positive real values.



Option, specified as Seed = n

Initializes the random generator with the integer seed n. n can also be the option CurrentTime, to make the seed depend on the current time.

This option serves for generating generators that return predictable sequences of pseudo-random numbers. The generator is initialized with the seed n which may be an arbitrary integer. Several generators with the same initial seed produce the same sequence of numbers.

When this option is used, the shape parameters a and b must be convertible to positive floating-point numbers at the time when the random generator is generated.

Return Values



The implemented algorithm for the computation of the beta deviates uses gamma deviates x, y to produce a beta deviate x/(x + y). For more information see: D. Knuth, Seminumerical Algorithms (1998), Vol. 2, p. 134.

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