public interface ProbabilityDistribution
Modifier and Type | Method and Description |
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double |
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.
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double |
variance()
Gets the variance of this distribution.
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double mean()
double median()
double variance()
double skew()
double kurtosis()
double entropy()
double cdf(double x)
x
- xdouble quantile(double u)
This may not always exist.F-1(u) = x, such that Pr(X ≤ x) = u
u
- u
, a quantiledouble density(double x)
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.
x
- xdouble moment(double t)
E(etX)This may not always exist.
t
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