RealDistribution (parent patrimoine de base SIRIUS 4.1 API)

All Known Implementing Classes:

AbstractRealDistribution, BetaDistribution, CauchyDistribution, ChiSquaredDistribution, EmpiricalDistribution, ExponentialDistribution, FDistribution, GammaDistribution, LogNormalDistribution, NormalDistribution, TDistribution, TriangularDistribution, UniformRealDistribution, WeibullDistribution
```
public interface RealDistribution
```
Base interface for distributions on the reals.

Since:

3.0

Version:

$Id: RealDistribution.java 18108 2017-10-04 06:45:27Z bignon $

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`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`	`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`	`getSupportLowerBound()` Access the lower bound of the support.
`double`	`getSupportUpperBound()` Access the upper bound of the support.
`double`	`inverseCumulativeProbability(double p)` Computes the quantile function of this distribution.
`boolean`	`isSupportConnected()` Use this method to get information about whether the support is connected, i.e.
`double`	`probability(double x)` For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X = x)`.
`double`	`probability(double x0, double x1)` For a random variable `X` whose values are distributed according to this distribution, this method returns `P(x0 < X <= x1)`.
`void`	`reseedRandomGenerator(long seed)` Reseed the random generator used to generate samples.
`double`	`sample()` Generate a random value sampled from this distribution.
`double[]`	`sample(int sampleSize)` Generate a random sample from the distribution.

- Method Detail
  - probability
```
double probability(double x)
```
    For a random variable X whose values are distributed according to this distribution, this method returns P(X = x). In other words, this method represents the probability mass function (PMF) for the distribution.
    
    Parameters:
    
    x - the point at which the PMF is evaluated
    
    Returns:
    
    the value of the probability mass function at point x
  - probability
```
double probability(double x0,
                   double x1)
```
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
    
    Parameters:
    
    x0 - Lower bound (excluded).
    
    x1 - Upper bound (included).
    
    Returns:
    
    the probability that a random variable with this distribution takes a value between x0 and x1, excluding the lower and including the upper endpoint.
    
    Throws:
    
    NumberIsTooLargeException - if x0 > x1. The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
    
    Since:
    
    3.1
  - density
```
double density(double x)
```
    Returns the probability density function (PDF) of this distribution evaluated at the specified point 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.
    
    Parameters:
    
    x - the point at which the PDF is evaluated
    
    Returns:
    
    the value of the probability density function at point x
  - cumulativeProbability
```
double cumulativeProbability(double x)
```
    For a random variable 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.
    
    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
  - inverseCumulativeProbability
```
double inverseCumulativeProbability(double p)
```
    Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is
    - inf{x in R | P(X<=x) >= p} for 0 < p <= 1,
    - inf{x in R | P(X<=x) > 0} for p = 0.
    Parameters:
    
    p - the cumulative probability
    
    Returns:
    
    the smallest p-quantile of this distribution (largest 0-quantile for p = 0)
    
    Throws:
    
    OutOfRangeException - if p < 0 or p > 1
  - getNumericalMean
```
double getNumericalMean()
```
    Use this method to get the numerical value of the mean of this distribution.
    
    Returns:
    
    the mean or Double.NaN if it is not defined
  - getNumericalVariance
```
double getNumericalVariance()
```
    Use this method to get the numerical value of the variance of this distribution.
    
    Returns:
    
    the variance (possibly Double.POSITIVE_INFINITY as for certain cases in TDistribution) or Double.NaN if it is not defined
  - getSupportLowerBound
```
double getSupportLowerBound()
```
    Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return
    inf {x in R | P(X <= x) > 0}.
    
    Returns:
    
    lower bound of the support (might be Double.NEGATIVE_INFINITY)
  - getSupportUpperBound
```
double getSupportUpperBound()
```
    Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return
    inf {x in R | P(X <= x) = 1}.
    
    Returns:
    
    upper bound of the support (might be Double.POSITIVE_INFINITY)
  - isSupportConnected
```
boolean 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
  - reseedRandomGenerator
```
void reseedRandomGenerator(long seed)
```
    Reseed the random generator used to generate samples.
    
    Parameters:
    
    seed - the new seed
  - sample
```
double sample()
```
    Generate a random value sampled from this distribution.
    
    Returns:
    
    a random value.
  - sample
```
double[] sample(int sampleSize)
```
    Generate a random sample from the distribution.
    
    Parameters:
    
    sampleSize - the number of random values to generate
    
    Returns:
    
    an array representing the random sample
    
    Throws:
    
    NotStrictlyPositiveException - if sampleSize is not positive

Interface RealDistribution

Method Summary

Method Detail

probability

probability

density

cumulativeProbability

inverseCumulativeProbability

getNumericalMean

getNumericalVariance

getSupportLowerBound

getSupportUpperBound

isSupportConnected

reseedRandomGenerator

sample

sample