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Generate a random number generator for log-normal deviates

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


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


stats::normalRandom(m, v) returns a procedure that produces lognormal deviates (random numbers) with location parameter m and shape parameter v > 0.

A random variable X is log-normally distributed if ln(X) is a normally distributed variable. The “location parameter” m of X is the mean of ln(X) and the “shape parameter” v is the variance of ln(X).

The procedure f := stats::lognormalRandom(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::lognormalRandom(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 log-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.


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::lognormalRandom(m, v): f() $k = 1..K rather than by stats::lognormalRandom(m, v)() $k = 1..K. The latter call produces a sequence of generators each of which is called once. Also note that stats::lognormalRandom(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.


Example 1

We generate log-normal deviates with location parameter 2 and shape parameter :

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::lognormalRandom(m, v):

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

m := PI/10: 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::lognormalRandom(1, 3, Seed = 1):
f() $ k = 1..4

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

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

delete f, g:



The location parameter: an arithmetical expression representing a real value


The shape parameter: an arithmetical expression representing a positive real value



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



The implementation uses stats::normalRandom.

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