Class NakagamiDistribution
- All Implemented Interfaces:
Serializable, RealDistribution
- Since:
- 3.4
- See Also:
-
Field Summary
FieldsModifier and TypeFieldDescriptionstatic final doubleDefault inverse cumulative probability accuracy.Fields inherited from class AbstractRealDistribution
random, randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY -
Constructor Summary
ConstructorsConstructorDescriptionNakagamiDistribution(double mu, double omega) Build a new instance.NakagamiDistribution(double mu, double omega, double inverseAbsoluteAccuracy) Build a new instance.NakagamiDistribution(RandomGenerator rng, double mu, double omega, double inverseAbsoluteAccuracy) Build a new instance. -
Method Summary
Modifier and TypeMethodDescriptiondoublecumulativeProbability(double x) For a random variableXwhose values are distributed according to this distribution, this method returnsP(X <= x).doubledensity(double x) Returns the probability density function (PDF) of this distribution evaluated at the specified pointx.doubleUse this method to get the numerical value of the mean of this distribution.doubleUse this method to get the numerical value of the variance of this distribution.doublegetScale()Access the scale parameter,omega.doublegetShape()Access the shape parameter,mu.protected doubleReturns the solver absolute accuracy for inverse cumulative computation.doubleAccess the lower bound of the support.doubleAccess the upper bound of the support.booleanUse this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support.booleanWhether or not the lower bound of support is in the domain of the density function.booleanWhether or not the upper bound of support is in the domain of the density function.Methods inherited from class AbstractRealDistribution
cumulativeProbability, inverseCumulativeProbability, logDensity, probability, probability, reseedRandomGenerator, sample, sample
-
Field Details
-
DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.- See Also:
-
-
Constructor Details
-
NakagamiDistribution
Build a new instance.Note: this constructor will implicitly create an instance of
Well19937cas random generator to be used for sampling only (seeAbstractRealDistribution.sample()andAbstractRealDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to passnullas random generator via the appropriate constructors to avoid the additional initialisation overhead.- Parameters:
mu- shape parameteromega- scale parameter (must be positive)- Throws:
NumberIsTooSmallException- ifmu < 0.5NotStrictlyPositiveException- ifomega <= 0
-
NakagamiDistribution
Build a new instance.Note: this constructor will implicitly create an instance of
Well19937cas random generator to be used for sampling only (seeAbstractRealDistribution.sample()andAbstractRealDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to passnullas random generator via the appropriate constructors to avoid the additional initialisation overhead.- Parameters:
mu- shape parameteromega- scale parameter (must be positive)inverseAbsoluteAccuracy- the maximum absolute error in inverse cumulative probability estimates (defaults toDEFAULT_INVERSE_ABSOLUTE_ACCURACY).- Throws:
NumberIsTooSmallException- ifmu < 0.5NotStrictlyPositiveException- ifomega <= 0
-
NakagamiDistribution
public NakagamiDistribution(RandomGenerator rng, double mu, double omega, double inverseAbsoluteAccuracy) Build a new instance.- Parameters:
rng- Random number generatormu- shape parameteromega- scale parameter (must be positive)inverseAbsoluteAccuracy- the maximum absolute error in inverse cumulative probability estimates (defaults toDEFAULT_INVERSE_ABSOLUTE_ACCURACY).- Throws:
NumberIsTooSmallException- ifmu < 0.5NotStrictlyPositiveException- ifomega <= 0
-
-
Method Details
-
getShape
-
getScale
-
getSolverAbsoluteAccuracy
Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.- Overrides:
getSolverAbsoluteAccuracyin classAbstractRealDistribution- Returns:
- the maximum absolute error in inverse cumulative probability estimates
-
density
Returns the probability density function (PDF) of this distribution evaluated at the specified pointx. In general, the PDF is the derivative of theCDF. If the derivative does not exist atx, 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.- Parameters:
x- the point at which the PDF is evaluated- Returns:
- the value of the probability density function at point
x
-
cumulativeProbability
For a random variableXwhose values are distributed according to this distribution, this method returnsP(X <= x). In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.- Parameters:
x- the point at which the CDF is evaluated- Returns:
- the probability that a random variable with this
distribution takes a value less than or equal to
x
-
getNumericalMean
Use this method to get the numerical value of the mean of this distribution.- Returns:
- the mean or
Double.NaNif it is not defined
-
getNumericalVariance
Use this method to get the numerical value of the variance of this distribution.- Returns:
- the variance (possibly
Double.POSITIVE_INFINITYas for certain cases inTDistribution) orDouble.NaNif it is not defined
-
getSupportLowerBound
Access the lower bound of the support. This method must return the same value asinverseCumulativeProbability(0). In other words, this method must returninf {x in R | P(X invalid input: '<'= x) > 0}.- Returns:
- lower bound of the support (might be
Double.NEGATIVE_INFINITY)
-
getSupportUpperBound
Access the upper bound of the support. This method must return the same value asinverseCumulativeProbability(1). In other words, this method must returninf {x in R | P(X invalid input: '<'= x) = 1}.- Returns:
- upper bound of the support (might be
Double.POSITIVE_INFINITY)
-
isSupportLowerBoundInclusive
Whether or not the lower bound of support is in the domain of the density function. Returns true iffgetSupporLowerBound()is finite anddensity(getSupportLowerBound())returns a non-NaN, non-infinite value.- Returns:
- true if the lower bound of support is finite and the density function returns a non-NaN, non-infinite value there
-
isSupportUpperBoundInclusive
Whether or not the upper bound of support is in the domain of the density function. Returns true iffgetSupportUpperBound()is finite anddensity(getSupportUpperBound())returns a non-NaN, non-infinite value.- Returns:
- true if the upper bound of support is finite and the density function returns a non-NaN, non-infinite value there
-
isSupportConnected
Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support.- Returns:
- whether the support is connected or not
-