public class GammaDistribution extends Object implements ProbabilityDistribution
dgamma, pgamma, qgamma, rgamma
.Constructor and Description |
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GammaDistribution(double k,
double theta)
Construct a Gamma distribution.
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Modifier and Type | Method and Description |
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double |
cdf(double x)
Gets the cumulative probability F(x) = Pr(X ≤ x).
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double |
density(double x)
The density function, which, if exists, is the derivative of F.
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double |
entropy()
Deprecated.
Not supported yet.
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double |
kurtosis()
Gets the excess kurtosis of this distribution.
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double |
mean()
Gets the mean of this distribution.
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double |
median()
Gets the median of this distribution.
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double |
moment(double t)
The moment generating function is the expected value of etX.
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double |
quantile(double u)
Gets the quantile, the inverse of the cumulative distribution function.
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double |
skew()
Gets the skewness of this distribution.
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double |
variance()
Gets the variance of this distribution.
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public GammaDistribution(double k, double theta)
k
- the shape parametertheta
- the scale parameterpublic double mean()
ProbabilityDistribution
mean
in interface ProbabilityDistribution
public double median()
ProbabilityDistribution
median
in interface ProbabilityDistribution
public double variance()
ProbabilityDistribution
variance
in interface ProbabilityDistribution
public double skew()
ProbabilityDistribution
skew
in interface ProbabilityDistribution
public double kurtosis()
ProbabilityDistribution
kurtosis
in interface ProbabilityDistribution
@Deprecated public double entropy()
ProbabilityDistribution
entropy
in interface ProbabilityDistribution
public double cdf(double x)
ProbabilityDistribution
cdf
in interface ProbabilityDistribution
x
- xpublic double quantile(double u)
ProbabilityDistribution
This may not always exist.F-1(u) = x, such that Pr(X ≤ x) = u
quantile
in interface ProbabilityDistribution
u
- u
, a quantilepublic double density(double x)
ProbabilityDistribution
f(x) = dF(X) / dxThis 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.
density
in interface ProbabilityDistribution
x
- xpublic double moment(double t)
ProbabilityDistribution
E(etX)This may not always exist.
moment
in interface ProbabilityDistribution
t
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