Interface ProbabilityDistribution
- All Known Subinterfaces:
UnivariateEVD
- All Known Implementing Classes:
ADFAsymptoticDistribution
,ADFAsymptoticDistribution1
,ADFDistribution
,ADFFiniteSampleDistribution
,BetaDistribution
,BinomialDistribution
,ChiSquareDistribution
,EmpiricalDistribution
,ExponentialDistribution
,FDistribution
,FisherExactDistribution
,FrechetDistribution
,GammaDistribution
,GeneralizedEVD
,GeneralizedParetoDistribution
,GumbelDistribution
,JarqueBeraDistribution
,JohansenAsymptoticDistribution
,KolmogorovDistribution
,KolmogorovOneSidedDistribution
,KolmogorovTwoSamplesDistribution
,LogNormalDistribution
,MaximaDistribution
,MinimaDistribution
,NormalDistribution
,OrderStatisticsDistribution
,PoissonDistribution
,RayleighDistribution
,ReversedWeibullDistribution
,ShapiroWilkDistribution
,TDistribution
,TriangularDistribution
,TruncatedNormalDistribution
,WeibullDistribution
,WilcoxonRankSumDistribution
,WilcoxonSignedRankDistribution
public interface ProbabilityDistribution
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).
\[
F(x) = Pr(X invalid input: '<' x)
\]
- See Also:
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Method Summary
Modifier and TypeMethodDescriptiondouble
cdf
(double x) Gets the cumulative probability F(x) = Pr(X ≤ x).double
density
(double x) The density function, which, if exists, is the derivative of F.double
entropy()
Gets the entropy of this distribution.double
kurtosis()
Gets the excess kurtosis of this distribution.double
mean()
Gets the mean of this distribution.double
median()
Gets the median of this distribution.double
moment
(double t) The moment generating function is the expected value of etX.double
quantile
(double u) Gets the quantile, the inverse of the cumulative distribution function.double
skew()
Gets the skewness of this distribution.double
variance()
Gets the variance of this distribution.
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Method Details
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mean
double mean()Gets the mean of this distribution.- Returns:
- the mean
- See Also:
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median
double median()Gets the median of this distribution.- Returns:
- the median
- See Also:
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variance
double variance()Gets the variance of this distribution.- Returns:
- the variance
- See Also:
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skew
double skew()Gets the skewness of this distribution.- Returns:
- the skewness
- See Also:
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kurtosis
double kurtosis()Gets the excess kurtosis of this distribution.- Returns:
- the excess kurtosis
- See Also:
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entropy
double entropy()Gets the entropy of this distribution.- Returns:
- the entropy
- See Also:
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cdf
double cdf(double x) Gets the cumulative probability F(x) = Pr(X ≤ x).- Parameters:
x
- x- Returns:
- F(x) = Pr(X ≤ x)
- See Also:
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quantile
double quantile(double u) Gets the quantile, the inverse of the cumulative distribution function. It is the value below which random draws from the distribution would fall u×100 percent of the time.
This may not always exist.F-1(u) = x, such that Pr(X ≤ x) = u
- Parameters:
u
-u
, a quantile- Returns:
- F-1(u)
- See Also:
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density
double density(double x) The density function, which, if exists, is the derivative of F. It describes the density of probability at each point in the sample space.f(x) = dF(X) / dx
This may not always exist. For the discrete cases, this is the probability mass function. It gives the probability that a discrete random variable is exactly equal to some value.- Parameters:
x
- x- Returns:
- f(x)
- See Also:
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moment
double moment(double t) The moment generating function is the expected value of etX. That is,E(etX)
This may not always exist.- Parameters:
t
- t- Returns:
- E(exp(tX))
- See Also:
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