Documentation Center

  • Trial Software
  • Product Updates

stats::normalRandom

Generate a random number generator for normal deviates

Use only in the MuPAD Notebook Interface.

This functionality does not run in MATLAB.

Syntax

stats::normalRandom(m, v, <Seed = s>)

Description

stats::normalRandom(m, v) returns a procedure that produces normal deviates (random numbers) with mean m and variance v > 0.

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

If m and v can be converted to floating-point numbers, f() returns a real floating point number. Otherwise, the symbolic call stats::normalRandom(m, v)() is returned.

Numerical values of m and v are only accepted if they are real and v is positive.

The values X = f() are distributed randomly according to the cumulative distribution function of the normal distribution with parameters m and v. For any real x, the probability that Xx is given by

.

Without the option Seed = s, 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.

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

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

f := stats::normalRandom(m, v): f() $k = 1..K;

rather than by

stats::normalRandom(m, v)() $k = 1..K;

The latter call produces a sequence of generators each of which is called once. Also note that

stats::normalRandom(m, v, 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.

Examples

Example 1

We generate normal deviates with mean 2 and variance :

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

delete f:

Example 2

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

f := stats::normalRandom(m, v): f()

When m and v evaluate to real numbers, f starts to produce random floating point numbers:

m := PI: v := 1/8: f() $ k = 1..4

delete f, m, v:

Example 3

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

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

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

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

delete f, g:

Parameters

m

The mean: an arithmetical expression representing a real value

v

The variance: an arithmetical expression representing a positive real value

Options

Seed

Option, specified as Seed = s

Initializes the random generator with the integer seed s. s 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 s 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 parameters m and v must be convertible to suitable floating-point numbers at the time when the random generator is generated.

Return Values

procedure.

Algorithms

The implemented algorithm for the computation of the normal deviates uses the Box-Mueller method. For more information see: D. Knuth, Seminumerical Algorithms (1998), Vol. 2, pp. 122.

See Also

MuPAD Functions

Was this topic helpful?