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betastat


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statistics: [ m , v ] = betastat ( a , b )

Compute statistics of the Beta distribution.

[ m , v ] = betastat ( a , b ) returns the mean
and variance of the Beta distribution with shape parameters a and
b .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the Beta distribution can be found at
https://en.wikipedia.org/wiki/Beta_distribution

See also:
betacdf,
betainv,
betapdf,
betarnd,
betafit,
betalike


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Compute statistics of the Beta distribution.



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binostat


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statistics: [ m , v ] = binostat ( n , ps )

Compute statistics of the binomial distribution.

[ m , v ] = binostat ( n , ps ) returns the mean and
variance of the binomial distribution with parameters n and ps ,
where n is the number of trials and ps is the probability of
success.

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the binomial distribution can be found at
https://en.wikipedia.org/wiki/Binomial_distribution

See also:
binocdf,
binoinv,
binopdf,
binornd,
binofit,
binolike,
binotest


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Compute statistics of the binomial distribution.



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bisastat


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statistics: [ m , v ] = bisastat ( beta , gamma )

Compute statistics of the Birnbaum-Saunders distribution.

[ m , v ] = bisastat ( beta , gamma ) returns the
mean and variance of the Birnbaum-Saunders distribution with scale parameter
beta and shape parameter gamma .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the Birnbaum-Saunders distribution can be found at
https://en.wikipedia.org/wiki/Birnbaum%E2%80%93Saunders_distribution

See also:
bisacdf,
bisainv,
bisapdf,
bisarnd,
bisafit,
bisalike


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Compute statistics of the Birnbaum-Saunders distribution.



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burrstat


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statistics: [ m , v ] = burrstat ( lambda , c , k )

Compute statistics of the Burr type XII distribution.

[ m , v ] = burrstat ( lambda , c , k ) returns
the mean and variance of the Burr type XII distribution with scale parameter
lambda , first shape parameter c , and second shape parameter
k .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the same
size as the other inputs.

Further information about the Burr distribution can be found at
https://en.wikipedia.org/wiki/Burr_distribution

See also:
gevcdf,
gevinv,
gevpdf,
gevrnd,
gevfit,
gevlike


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Compute statistics of the Burr type XII distribution.



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chi2stat


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statistics: [ m , v ] = chi2stat ( df )

Compute statistics of the chi-squared distribution.

[ m , v ] = chi2stat ( df ) returns the mean and
variance of the chi-squared distribution with df degrees of freedom.

The size of m (mean) and v (variance) is the same size of the
input argument.

Further information about the chi-squared distribution can be found at
https://en.wikipedia.org/wiki/Chi-squared_distribution

See also:
chi2cdf,
chi2inv,
chi2pdf,
chi2rnd


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Compute statistics of the chi-squared distribution.



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copulaparam


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statistics: param = copulaparam ( family , r )
statistics: param = copulaparam (&hellip;, "type" , type )

Copula parameter as a function of rank correlation.

param = copulaparam ( family , r ) returns the linear
or copula parameter param corresponding to a copula of the family
family that has Kendall&rsquo;s rank correlation r . It is the inverse
of copulastat .

family is the copula family name. It can be "Gaussian" for the
Gaussian family, "t" for the Student&rsquo;s t family, "Clayton"
for the Clayton family, "Gumbel" for the Gumbel-Hougaard family,
"Frank" for the Frank family, "AMH" for the Ali-Mikhail-Haq
family, or "FGM" for the Farlie-Gumbel-Morgenstern family. The last
two are Octave extensions that MATLAB does not provide, and are treated as
bivariate. Neither reaches the whole range of either rank correlation: the
Ali-Mikhail-Haq family covers a Kendall&rsquo;s tau in
[(5-8*log (2))/3, 1/3] and the Farlie-Gumbel-Morgenstern family one
in [-2/9, 2/9] .

For the Gaussian and Student&rsquo;s t families, r is a scalar rank
correlation or a matrix of pairwise rank correlations, and param is the
corresponding linear correlation of the same size. For the Clayton,
Gumbel-Hougaard, and Frank families, r is a scalar rank correlation and
param is the scalar copula parameter. The Gumbel-Hougaard family
models positive dependence only, so r must be non-negative for that
family.

param = copulaparam (&hellip;, "type" , type )
selects the measure of rank correlation given in r . type can be
"Kendall" (the default) for Kendall&rsquo;s tau, or "Spearman" for
Spearman&rsquo;s rho.

The Gaussian and Student&rsquo;s t families and the Kendall&rsquo;s tau of the Clayton
and Gumbel-Hougaard families are inverted in closed form. The remaining
cases are inverted numerically from copulastat .

Note: for the Archimedean families with "Spearman" , the
underlying relationship is computed by exact numerical integration rather
than the interpolated table used by MATLAB , so results may differ from
MATLAB by up to about 10^{-4} . See copulastat .

See also:
copulastat,
copulafit,
copulacdf,
copulapdf,
copularnd


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Copula parameter as a function of rank correlation.



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copulastat


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statistics: r = copulastat ( family , param )
statistics: r = copulastat (&hellip;, "type" , type )

Rank correlation for a copula family.

r = copulastat ( family , param ) returns Kendall&rsquo;s
rank correlation r corresponding to a copula of the family
family with linear or copula parameter param .

family is the copula family name. It can be "Gaussian" for the
Gaussian family, "t" for the Student&rsquo;s t family, "Clayton"
for the Clayton family, "Gumbel" for the Gumbel-Hougaard family,
"Frank" for the Frank family, "AMH" for the Ali-Mikhail-Haq
family, or "FGM" for the Farlie-Gumbel-Morgenstern family. The last
two are Octave extensions that MATLAB does not provide, and are treated as
bivariate. Neither reaches the whole range of either rank correlation: the
Ali-Mikhail-Haq family covers a Kendall&rsquo;s tau in
[(5-8*log (2))/3, 1/3] and the Farlie-Gumbel-Morgenstern family one
in [-2/9, 2/9] .

For the Gaussian and Student&rsquo;s t families, param is a linear
correlation coefficient rho in the range [-1,1] , or a
p -by- p correlation matrix, in which case r has the same
size and each element is computed elementwise. For the Clayton,
Gumbel-Hougaard, and Frank families, param is the scalar copula
parameter.

r = copulastat (&hellip;, "type" , type ) selects the
measure of rank correlation. type can be "Kendall" (the
default) for Kendall&rsquo;s tau, or "Spearman" for Spearman&rsquo;s rho.

The relationships are closed form for the Gaussian and Student&rsquo;s t families
( r = 2 \arcsin(\rho) / \pi for Kendall&rsquo;s tau and
r = 6 \arcsin(\rho/2) / \pi for Spearman&rsquo;s rho) and for the Kendall&rsquo;s
tau of the Archimedean families. Spearman&rsquo;s rho of the Archimedean families
has no closed form and is computed by accurate numerical integration of the
copula.

Note: MATLAB returns the Archimedean Spearman&rsquo;s rho by
interpolating an internal precomputed table, whose values deviate from the
true relationship by up to about 10^{-4} . This implementation returns
the mathematically exact value instead, so results for
copulastat ( family , param , "type", "Spearman") with an
Archimedean family may differ from MATLAB at that level.

See also:
copulaparam,
copulafit,
copulacdf,
copulapdf,
copularnd


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Rank correlation for a copula family.



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evstat


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statistics: [ m , v ] = evstat ( mu , sigma )

Compute statistics of the extreme value distribution.

[ m , v ] = evstat ( mu , sigma ) returns the mean
and variance of the extreme value distribution (also known as the Gumbel
or the type I generalized extreme value distribution) with location parameter
mu and scale parameter sigma .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

The type 1 extreme value distribution is also known as the Gumbel
distribution. This version is suitable for modeling minima. The mirror image
of this distribution can be used to model maxima by negating x . If
y has a Weibull distribution, then x = log ( y ) has
the type 1 extreme value distribution.

Further information about the Gumbel distribution can be found at
https://en.wikipedia.org/wiki/Gumbel_distribution

See also:
evcdf,
evinv,
evpdf,
evrnd,
evfit,
evlike


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Compute statistics of the extreme value distribution.



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expstat


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statistics: [ m , v ] = expstat ( mu )

Compute statistics of the exponential distribution.

[ m , v ] = expstat ( mu ) returns the mean and
variance of the exponential distribution with mean parameter mu .

The size of m (mean) and v (variance) is the same size of the
input argument.

A common alternative parameterization of the exponential distribution is to
use the parameter λ defined as the mean number of events in an
interval as opposed to the parameter μ , which is the mean wait time
for an event to occur. λ and μ are reciprocals,
i.e. μ = 1 / λ .

Further information about the exponential distribution can be found at
https://en.wikipedia.org/wiki/Exponential_distribution

See also:
expcdf,
expinv,
exppdf,
exprnd,
expfit,
explike


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Compute statistics of the exponential distribution.



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fstat


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statistics: [ m , v ] = fstat ( df1 , df2 )

Compute statistics of the F -distribution.

[ m , v ] = fstat ( df1 , df2 ) returns the mean and
variance of the F -distribution with df1 and df2 degrees
of freedom.

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the F -distribution can be found at
https://en.wikipedia.org/wiki/F-distribution

See also:
fcdf,
finv,
fpdf,
frnd


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Compute statistics of the F-distribution.



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gamstat


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statistics: [ m , v ] = gamstat ( a , b )

Compute statistics of the Gamma distribution.

[ m , v ] = gamstat ( a , b ) returns the mean
and variance of the Gamma distribution with shape parameter a and
scale parameter b .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

OCTAVE/MATLAB use the alternative parameterization given by the pair
α, β , i.e. shape a and scale b . In Wikipedia, the two
common parameterizations use the pairs k, θ , as shape and scale, and
α, β , as shape and rate, respectively. The parameter names a
and b used here (for MATLAB compatibility) correspond to the parameter
notation k, θ instead of the α, β as reported in Wikipedia.

Further information about the Gamma distribution can be found at
https://en.wikipedia.org/wiki/Gamma_distribution

See also:
gamcdf,
gaminv,
gampdf,
gamrnd,
gamfit,
gamlike


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Compute statistics of the Gamma distribution.



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geostat


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statistics: [ m , v ] = geostat ( ps )

Compute statistics of the geometric distribution.

[ m , v ] = geostat ( ps ) returns the mean and
variance of the geometric distribution with probability of success parameter
ps .

The size of m (mean) and v (variance) is the same size of the
input argument.

Further information about the geometric distribution can be found at
https://en.wikipedia.org/wiki/Geometric_distribution

See also:
geocdf,
geoinv,
geopdf,
geornd,
geofit


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Compute statistics of the geometric distribution.



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gevstat


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statistics: [ m , v ] = gevstat ( k , sigma , mu )

Compute statistics of the generalized extreme value distribution.

[ m , v ] = gevstat ( k , sigma , mu ) returns
the mean and variance of the generalized extreme value distribution with
shape parameter k , scale parameter sigma , and location parameter
mu .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

The mean of the GEV distribution is not finite when k >= 1 , and
the variance is not finite when k >= 1/2 . The GEV distribution
has positive density only for values of x such that
k * ( x - mu ) / sigma > -1 .

Further information about the generalized extreme value distribution can be
found at
https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution

See also:
gevcdf,
gevinv,
gevpdf,
gevrnd,
gevfit,
gevlike


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Compute statistics of the generalized extreme value distribution.



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gpstat


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statistics: [ m , v ] = gpstat ( k , sigma , theta )

Compute statistics of the generalized Pareto distribution.

[ m , v ] = gpstat ( k , sigma , theta )
returns the mean and variance of the generalized Pareto distribution with
shape parameter k , scale parameter sigma , and location parameter
theta .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

When k = 0 and theta = 0, the generalized Pareto distribution is
equivalent to the exponential distribution. When k > 0 and
theta = sigma / k , the generalized Pareto
distribution is equivalent to the Pareto distribution. The mean of the
generalized Pareto distribution is not finite when k >= 1 , and
the variance is not finite when k >= 1/2 . When
k >= 0 , the generalized Pareto distribution has positive density
for x > theta , or, when k < 0 , for
0 <= ( x - theta ) / sigma <= -1 / k .

Further information about the generalized Pareto distribution can be found at
https://en.wikipedia.org/wiki/Generalized_Pareto_distribution

See also:
gpcdf,
gpinv,
gppdf,
gprnd,
gpfit,
gplike


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Compute statistics of the generalized Pareto distribution.



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hnstat


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statistics: [ m , v ] = hnstat ( mu , sigma )

Compute statistics of the half-normal distribution.

[ m , v ] = hnstat ( mu , sigma ) returns the mean
and variance of the half-normal distribution with non-centrality (distance)
parameter mu and scale parameter sigma .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the half-normal distribution can be found at
https://en.wikipedia.org/wiki/Half-normal_distribution

See also:
hncdf,
hninv,
hnpdf,
hnrnd,
hnfit,
hnlike


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Compute statistics of the half-normal distribution.



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hygestat


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statistics: [ mn , v ] = hygestat ( m , k , n )

Compute statistics of the hypergeometric distribution.

[ mn , v ] = hygestat ( m , k , n ) returns the
mean and variance of the hypergeometric distribution parameters m ,
k , and n .

m is the total size of the population of the hypergeometric
distribution. The elements of m must be positive natural numbers.

k is the number of marked items of the hypergeometric distribution.
The elements of k must be natural numbers.

n is the size of the drawn sample of the hypergeometric
distribution. The elements of n must be positive natural numbers.

The size of mn (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the hypergeometric distribution can be found at
https://en.wikipedia.org/wiki/Hypergeometric_distribution

See also:
hygecdf,
hygeinv,
hygepdf,
hygernd


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Compute statistics of the hypergeometric distribution.



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invgstat


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statistics: [ m , v ] = invgstat ( mu , lambda )

Compute statistics of the inverse Gaussian distribution.

[ m , v ] = invgstat ( mu , lambda ) returns the
mean and variance of the inverse Gaussian distribution with mean parameter
mu and shape parameter lambda .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the inverse Gaussian distribution can be found at
https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution

See also:
invgcdf,
invginv,
invgpdf,
invgrnd,
invgfit,
invglike


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Compute statistics of the inverse Gaussian distribution.



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logistat


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statistics: [ m , v ] = logistat ( mu , sigma )

Compute statistics of the logistic distribution.

[ m , v ] = logistat ( mu , sigma ) returns the mean
and variance of the logistic distribution with mean parameter mu and
scale parameter sigma .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the logistic distribution can be found at
https://en.wikipedia.org/wiki/Logistic_distribution

See also:
logicdf,
logiinv,
logipdf,
logirnd,
logifit,
logilike


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Compute statistics of the logistic distribution.



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loglstat


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statistics: [ m , v ] = loglstat ( mu , sigma )

Compute statistics of the loglogistic distribution.

[ m , v ] = loglstat ( mu , sigma ) returns the mean
and variance of the loglogistic distribution with mean parameter mu and
scale parameter sigma .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the loglogistic distribution can be found at
https://en.wikipedia.org/wiki/Log-logistic_distribution

OCTAVE/MATLAB use an alternative parameterization given by the pair
μ, σ , i.e. mu and sigma , in analogy with the logistic
distribution. Their relation to the α and b parameters used
in Wikipedia are given below:

mu = log ( a )
sigma = 1 / a

See also:
logncdf,
logninv,
lognpdf,
lognrnd,
lognfit,
lognlike


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Compute statistics of the loglogistic distribution.



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lognstat


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statistics: [ m , v ] = lognstat ( mu , sigma )

Compute statistics of the lognormal distribution.

[ m , v ] = lognstat ( mu , sigma ) returns the mean
and variance of the lognormal distribution with mean parameter mu and
standard deviation parameter sigma , each corresponding to the
associated normal distribution.

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the lognormal distribution can be found at
https://en.wikipedia.org/wiki/Log-normal_distribution

See also:
logncdf,
logninv,
lognpdf,
lognrnd,
lognfit,
lognlike


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Compute statistics of the lognormal distribution.



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# elements: 1
# length: 8
nakastat


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statistics: [ m , v ] = nakastat ( mu , omega )

Compute statistics of the Nakagami distribution.

[ m , v ] = nakastat ( mu , omega ) returns the mean
and variance of the Nakagami distribution with shape parameter mu and
spread parameter omega .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the Nakagami distribution can be found at
https://en.wikipedia.org/wiki/Normal_distribution

See also:
nakacdf,
nakainv,
nakapdf,
nakarnd,
nakafit,
nakalike


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Compute statistics of the Nakagami distribution.



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# length: 8
nbinstat


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# length: 760
statistics: [ m , v ] = nbinstat ( r , ps )

Compute statistics of the negative binomial distribution.

[ m , v ] = nbinstat ( r , ps ) returns the mean
and variance of the negative binomial distribution with parameters r
and ps , where r is the number of successes until the experiment
is stopped and ps is the probability of success in each experiment,
given the number of failures in x .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the negative binomial distribution can be found at
https://en.wikipedia.org/wiki/Negative_binomial_distribution

See also:
nbincdf,
nbininv,
nbinpdf,
nbinrnd,
nbinfit,
nbinlike


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# elements: 1
# length: 57
Compute statistics of the negative binomial distribution.



# name: <cell-element>
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# elements: 1
# length: 7
ncfstat


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# length: 636
statistics: [ m , v ] = ncfstat ( df1 , df1 , lambda )

Compute statistics for the noncentral F -distribution.

[ m , v ] = ncfstat ( df1 , df1 , lambda )
returns the mean and variance of the noncentral F -distribution with
df1 and df2 degrees of freedom and noncentrality parameter
lambda .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the same
size as the other inputs.

Further information about the noncentral F -distribution can be found
at https://en.wikipedia.org/wiki/Noncentral_F-distribution

See also:
ncfcdf,
ncfinv,
ncfpdf,
ncfrnd,
fstat


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Compute statistics for the noncentral F-distribution.



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nctstat


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statistics: [ m , v ] = nctstat ( df , mu )

Compute statistics for the noncentral t -distribution.

[ m , v ] = nctstat ( df , mu ) returns the mean
and variance of the noncentral t -distribution with df degrees
of freedom and noncentrality parameter mu .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the same
size as the other inputs.

Further information about the noncentral t -distribution can be found
at https://en.wikipedia.org/wiki/Noncentral_t-distribution

See also:
nctcdf,
nctinv,
nctpdf,
nctrnd,
tstat


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Compute statistics for the noncentral t-distribution.



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ncx2stat


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statistics: [ m , v ] = ncx2stat ( df , lambda )

Compute statistics for the noncentral chi-squared distribution.

[ m , v ] = ncx2stat ( df , lambda ) returns the
mean and variance of the noncentral chi-squared distribution with df
degrees of freedom and noncentrality parameter lambda .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the noncentral chi-squared distribution can be
found at
https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution

See also:
ncx2cdf,
ncx2inv,
ncx2pdf,
ncx2rnd


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Compute statistics for the noncentral chi-squared distribution.



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normstat


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statistics: [ m , v ] = normstat ( mu , sigma )

Compute statistics of the normal distribution.

[ m , v ] = normstat ( mu , sigma ) returns the mean
and variance of the normal distribution with non-centrality (distance)
parameter mu and scale parameter sigma .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the normal distribution can be found at
https://en.wikipedia.org/wiki/Normal_distribution

See also:
normcdf,
norminv,
normpdf,
normrnd,
normfit,
normlike


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Compute statistics of the normal distribution.



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plstat


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statistics: [ m , v ] = plstat ( x , Fx )

Compute statistics of the piecewise linear distribution.

[ m , v ] = plstat ( x , Fx ) returns the mean,
m , and variance, v , of the piecewise linear distribution with a
vector of x values at which the CDF changes slope and a vector of CDF
values Fx that correspond to each value in x . Both x and
Fx must be vectors of the same size and at least 2-elements long.

Further information about the piecewise linear distribution can be found at
https://en.wikipedia.org/wiki/Piecewise_linear_function

See also:
plcdf,
plinv,
plpdf,
plrnd


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Compute statistics of the piecewise linear distribution.



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poisstat


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statistics: [ m , v ] = poisstat ( lambda )

Compute statistics of the Poisson distribution.

[ m , v ] = poisstat ( lambda ) returns the mean and
variance of the Poisson distribution with rate parameter lambda .

The size of m (mean) and v (variance) is the same size of the
input argument.

Further information about the Poisson distribution can be found at
https://en.wikipedia.org/wiki/Poisson_distribution

See also:
poisscdf,
poissinv,
poisspdf,
poissrnd,
poissfit,
poisslike


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Compute statistics of the Poisson distribution.



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raylstat


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statistics: [ m , v ] = raylstat ( sigma )

Compute statistics of the Rayleigh distribution.

[ m , v ] = raylstat ( sigma ) returns the mean and
variance of the Rayleigh distribution with scale parameter sigma .

The size of m (mean) and v (variance) is the same size of the
input argument.

Further information about the Rayleigh distribution can be found at
https://en.wikipedia.org/wiki/Rayleigh_distribution

The prob.RayleighDistribution class names this same parameter
B , after MATLAB.

See also:
raylcdf,
raylinv,
raylpdf,
raylrnd,
raylfit,
rayllike


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Compute statistics of the Rayleigh distribution.



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ricestat


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statistics: [ m , v ] = ricestat ( s , sigma )

Compute statistics of the Rician distribution.

[ m , v ] = ricestat ( s , sigma ) returns the mean
and variance of the Rician distribution with non-centrality (distance)
parameter s and scale parameter sigma .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the Rician distribution can be found at
https://en.wikipedia.org/wiki/Rice_distribution

See also:
ricecdf,
riceinv,
ricepdf,
ricernd,
ricefit,
ricelike


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Compute statistics of the Rician distribution.



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tlsstat


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statistics: [ m , v ] = tlsstat ( mu , sigma , nu )

Compute statistics of the location-scale Student&rsquo;s T distribution.

[ m , v ] = tlsstat ( mu , sigma , nu ) returns
the mean and variance of the location-scale Student&rsquo;s T distribution with
location parameter mu , scale parameter sigma , and nu
degrees of freedom.

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the location-scale Student&rsquo;s T distribution can be
found at
https://en.wikipedia.org/wiki/Student%27s_t-distribution#Location-scale_t_distribution

See also:
tlscdf,
tlsinv,
tlspdf,
tlsrnd,
tlsfit,
tlslike


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Compute statistics of the location-scale Student's T distribution.



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tristat


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statistics: [ m , v ] = tristat ( a , b , c )

Compute statistics of the Triangular distribution.

[ m , v ] = tristat ( a , b , c ) returns the
mean and variance of the Triangular distribution with lower limit parameter
a , peak location (mode) parameter b , and upper limit parameter
c .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Note that the order of the parameter input arguments has been changed after
statistics version 1.6.3 in order to be MATLAB compatible with the parameters
used in the TriangularDistribution probability distribution object. More
specifically, the positions of the parameters b and c have been
swapped. As a result, the naming conventions no longer coincide with those
used in Wikipedia, in which b denotes the upper limit and c
denotes the mode or peak parameter.

Further information about the triangular distribution can be found at
https://en.wikipedia.org/wiki/Triangular_distribution

See also:
tcdf,
tinv,
tpdf,
trnd


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Compute statistics of the Triangular distribution.



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tstat


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statistics: [ m , v ] = tstat ( df )

Compute statistics of the Student&rsquo;s T distribution.

[ m , v ] = tstat ( df ) returns the mean and variance of
the Student&rsquo;s T distribution with df degrees of freedom.

The size of m (mean) and v (variance) is the same size of the
input argument.

Further information about the Student&rsquo;s T distribution can be found at
https://en.wikipedia.org/wiki/Student%27s_t-distribution

See also:
tcdf,
tinv,
tpdf,
trnd


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Compute statistics of the Student's T distribution.



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unidstat


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statistics: [ m , v ] = unidstat ( df )

Compute statistics of the discrete uniform cumulative distribution.

[ m , v ] = unidstat ( df ) returns the mean and variance
of the discrete uniform cumulative distribution with parameter N , which
corresponds to the maximum observable value and must be a positive natural
number.

The size of m (mean) and v (variance) is the same size of the
input argument.

Further information about the discrete uniform distribution can be found at
https://en.wikipedia.org/wiki/Discrete_uniform_distribution

See also:
unidcdf,
unidinv,
unidpdf,
unidrnd,
unidfit


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Compute statistics of the discrete uniform cumulative distribution.



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unifstat


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statistics: [ m , v ] = unifstat ( df )

Compute statistics of the continuous uniform cumulative distribution.

[ m , v ] = unifstat ( df ) returns the mean and variance
of the continuous uniform cumulative distribution with parameters a and
b , which define the lower and upper bounds of the interval
[ a , b ] .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the continuous uniform distribution can be found at
https://en.wikipedia.org/wiki/Continuous_uniform_distribution

See also:
unifcdf,
unifinv,
unifpdf,
unifrnd,
unifit


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Compute statistics of the continuous uniform cumulative distribution.



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wblstat


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statistics: [ m , v ] = wblstat ( lambda , k )

Compute statistics of the Weibull distribution.

[ m , v ] = wblstat ( lambda , k ) returns the mean
and variance of the Weibull distribution with scale parameter lambda
and shape parameter k .

The size of m (mean) and v (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.

Further information about the Weibull distribution can be found at
https://en.wikipedia.org/wiki/Weibull_distribution

The prob.WeibullDistribution class names these same two parameters
A and B , after MATLAB. lambda is its A and
k is its B .

See also:
wblcdf,
wblinv,
wblpdf,
wblrnd,
wblfit,
wbllike,
wblplot


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Compute statistics of the Weibull distribution.





