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java.lang.Objectorg.apache.commons.math3.distribution.AbstractRealDistribution
org.apache.commons.math3.distribution.GammaDistribution
public class GammaDistribution
Implementation of the Gamma distribution.
| Field Summary | |
|---|---|
static double |
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy. |
| Fields inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution |
|---|
random, randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY |
| Constructor Summary | |
|---|---|
GammaDistribution(double shape,
double scale)
Creates a new gamma distribution with specified values of the shape and scale parameters. |
|
GammaDistribution(double shape,
double scale,
double inverseCumAccuracy)
Creates a new gamma distribution with specified values of the shape and scale parameters. |
|
GammaDistribution(RandomGenerator rng,
double shape,
double scale,
double inverseCumAccuracy)
Creates a Gamma distribution. |
|
| Method Summary | |
|---|---|
double |
cumulativeProbability(double x)
For a random variable X whose values are distributed according
to this distribution, this method returns P(X <= x). |
double |
density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified point x. |
double |
getAlpha()
Deprecated. as of version 3.1, getShape() should be preferred.
This method will be removed in version 4.0. |
double |
getBeta()
Deprecated. as of version 3.1, getScale() should be preferred.
This method will be removed in version 4.0. |
double |
getNumericalMean()
Use this method to get the numerical value of the mean of this distribution. |
double |
getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution. |
double |
getScale()
Returns the scale parameter of this distribution. |
double |
getShape()
Returns the shape parameter of this distribution. |
protected double |
getSolverAbsoluteAccuracy()
Returns the solver absolute accuracy for inverse cumulative computation. |
double |
getSupportLowerBound()
Access the lower bound of the support. |
double |
getSupportUpperBound()
Access the upper bound of the support. |
boolean |
isSupportConnected()
Use this method to get information about whether the support is connected, i.e. |
boolean |
isSupportLowerBoundInclusive()
Whether or not the lower bound of support is in the domain of the density function. |
boolean |
isSupportUpperBoundInclusive()
Whether or not the upper bound of support is in the domain of the density function. |
double |
sample()
This implementation uses the following algorithms: |
| Methods inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution |
|---|
cumulativeProbability, inverseCumulativeProbability, probability, probability, reseedRandomGenerator, sample |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
| Constructor Detail |
|---|
public GammaDistribution(double shape,
double scale)
throws NotStrictlyPositiveException
shape - the shape parameterscale - the scale parameter
NotStrictlyPositiveException - if shape <= 0 or
scale <= 0.
public GammaDistribution(double shape,
double scale,
double inverseCumAccuracy)
throws NotStrictlyPositiveException
shape - the shape parameterscale - the scale parameterinverseCumAccuracy - the maximum absolute error in inverse
cumulative probability estimates (defaults to
DEFAULT_INVERSE_ABSOLUTE_ACCURACY).
NotStrictlyPositiveException - if shape <= 0 or
scale <= 0.
public GammaDistribution(RandomGenerator rng,
double shape,
double scale,
double inverseCumAccuracy)
throws NotStrictlyPositiveException
rng - Random number generator.shape - the shape parameterscale - the scale parameterinverseCumAccuracy - the maximum absolute error in inverse
cumulative probability estimates (defaults to
DEFAULT_INVERSE_ABSOLUTE_ACCURACY).
NotStrictlyPositiveException - if shape <= 0 or
scale <= 0.| Method Detail |
|---|
@Deprecated public double getAlpha()
getShape() should be preferred.
This method will be removed in version 4.0.
this distribution.
public double getShape()
this distribution.
@Deprecated public double getBeta()
getScale() should be preferred.
This method will be removed in version 4.0.
this distribution.
public double getScale()
this distribution.
public double density(double x)
x. In general, the PDF is
the derivative of the CDF.
If the derivative does not exist at x, then an appropriate
replacement should be returned, e.g. Double.POSITIVE_INFINITY,
Double.NaN, or the limit inferior or limit superior of the
difference quotient.
x - the point at which the PDF is evaluated
xpublic double cumulativeProbability(double x)
X whose values are distributed according
to this distribution, this method returns P(X <= x). In other
words, this method represents the (cumulative) distribution function
(CDF) for this distribution.
The implementation of this method is based on:
x - the point at which the CDF is evaluated
xprotected double getSolverAbsoluteAccuracy()
getSolverAbsoluteAccuracy in class AbstractRealDistributionpublic double getNumericalMean()
alpha and scale parameter beta, the
mean is alpha * beta.
Double.NaN if it is not definedpublic double getNumericalVariance()
alpha and scale parameter beta, the
variance is alpha * beta^2.
Double.POSITIVE_INFINITY as
for certain cases in TDistribution) or Double.NaN if it
is not definedpublic double getSupportLowerBound()
inverseCumulativeProbability(0). In other words, this
method must return
inf {x in R | P(X <= x) > 0}.
public double getSupportUpperBound()
inverseCumulativeProbability(1). In other words, this
method must return
inf {x in R | P(X <= x) = 1}.
public boolean isSupportLowerBoundInclusive()
getSupporLowerBound() is finite and
density(getSupportLowerBound()) returns a non-NaN, non-infinite
value.
public boolean isSupportUpperBoundInclusive()
getSupportUpperBound() is finite and
density(getSupportUpperBound()) returns a non-NaN, non-infinite
value.
public boolean isSupportConnected()
truepublic double sample()
This implementation uses the following algorithms:
For 0 < shape < 1:
Ahrens, J. H. and Dieter, U., Computer methods for
sampling from gamma, beta, Poisson and binomial distributions.
Computing, 12, 223-246, 1974.
For shape >= 1:
Marsaglia and Tsang, A Simple Method for Generating
Gamma Variables. ACM Transactions on Mathematical Software,
Volume 26 Issue 3, September, 2000.
sample in interface RealDistributionsample in class AbstractRealDistribution
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