stats
::weibullRandom
Generate a random number generator for Weibull 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::weibullRandom(a
, b
, <Seed = s
>)
stats::weibullRandom(a, b)
returns a procedure
that produces Weibull deviates
(random numbers) with shape parameter a
> 0 and
scale parameter b
> 0.
The procedure f := stats::weibullRandom(a, b)
can
be called in the form f()
. The return value of f()
is
either a floatingpoint number or a symbolic expression:
If a
and b
can be converted
to positive floatingpoint numbers, then f()
returns
a nonnegative floatingpoint number.
In all other cases, stats::weibullRandom(a, b)()
is
returned symbolically.
Numerical values of a
and b
are
only accepted if they are real and positive.
The values X = f()
are distributed randomly
according to the cumulative distribution function of the Weibull distribution
with parameters a
and b
. For
any 0 ≤ x,
the probability that X ≤ x 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 reinitialized with the reset
function, random generators produce
the same sequences of numbers.
In contrast to the function random
, the generators produced by stats::weibullRandom
do
not react to the environment variable SEED
.
For efficiency, it is recommended to produce sequences of K random numbers via
f := stats::weibullRandom(a, b): f() $k = 1..K;
rather than by
stats::weibullRandom(a, b)() $k = 1..K;
The latter call produces a sequence of generators each of which is called once. Also note that
stats::weibullRandom(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.
The function is sensitive to the environment variable DIGITS
which
determines the numerical working precision.
We generate Weibull deviates with parameters a
= 2 and b
= :
f := stats::weibullRandom(2, 3/4): f() $ k = 1..4
delete f:
With symbolic parameters, no random floatingpoint numbers can be produced:
f := stats::weibullRandom(a, b): f()
When positive real numbers are assigned to a
and b
,
the function f
starts to produce random floating
point numbers:
a := PI: b := 1/8: f() $ k = 1..4
delete f, a, b:
We use the option Seed
= s
to
reproduce a sequence of random numbers:
f := stats::weibullRandom(PI, 3, Seed = 1): f() $ k = 1..4
g := stats::weibullRandom(PI, 3, Seed = 1): g() $ k = 1..4
f() = g(), f() = g()
delete f, g:

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

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

Option, specified as Initializes the random generator with the integer seed This option serves for generating generators that return predictable
sequences of pseudorandom numbers. The generator is initialized with
the seed When this option is used, the parameters 
The implemented algorithm for the computation of the Weibull deviates uses the quantile function of the Weibull distribution applied to unformly distributed random numbers on the interval .