org.ojalgo.random
Class LogNormal

java.lang.Object
  java.lang.Number
      org.ojalgo.random.RandomNumber
          org.ojalgo.random.LogNormal

All Implemented Interfaces:

Serializable, Function<Double>, NullaryFunction<Double>, ContinuousDistribution, Distribution

public class LogNormal
extends RandomNumber

A continuous distribution in which the logarithm of a variable has a normal distribution. A log normal distribution results if the variable is the product of a large number of independent, identically-distributed variables in the same way that a normal distribution results if the variable is the sum of a large number of independent, identically-distributed variables.

Author:

apete

See Also:

Serialized Form

Constructor Summary

LogNormal()

LogNormal(double aMean, double aStdDev)
The aMean and aStdDev parameters are the mean and standard deviation of the variable's logarithm (by definition, the variable's logarithm is normally distributed).

Method Summary

static LogNormal	estimate(Access1D<?> rawSamples)
double	getDistribution(double aValue) In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x.
double	getExpected()
double	getGeometricMean() The geometric mean is also the median
double	getGeometricStandardDeviation()
double	getLowerConfidenceQuantile(double aConfidence)
double	getProbability(double aValue) In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point.
double	getQuantile(double aProbality) The quantile function, for any distribution, is defined for real variables between zero and one and is mathematically the inverse of the cumulative distribution function.
double	getUpperConfidenceQuantile(double aConfidence)
double	getVariance() Subclasses must override either getStandardDeviation() or getVariance()!
static LogNormal	make(double aExpected, double aVariance)

Methods inherited from class org.ojalgo.random.RandomNumber

doubleValue, floatValue, getStandardDeviation, intValue, invoke, longValue, toString

Methods inherited from class java.lang.Number

byteValue, shortValue

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.ojalgo.random.Distribution

getStandardDeviation

Constructor Detail

LogNormal

public LogNormal()

LogNormal

public LogNormal(double aMean,
                 double aStdDev)

The aMean and aStdDev parameters are the mean and standard deviation of the variable's logarithm (by definition, the variable's logarithm is normally distributed).

Method Detail

estimate

public static LogNormal estimate(Access1D<?> rawSamples)

make

public static LogNormal make(double aExpected,
                             double aVariance)

getDistribution

public double getDistribution(double aValue)

Description copied from interface: ContinuousDistribution

In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far" function of the probability distribution. Cumulative distribution functions are also used to specify the distribution of multivariate random variables. WikipediA

Parameters:

aValue - x

Returns:

P(<=x)

getExpected

public double getExpected()

getGeometricMean

public double getGeometricMean()

The geometric mean is also the median

getGeometricStandardDeviation

public double getGeometricStandardDeviation()

getProbability

public double getProbability(double aValue)

Description copied from interface: ContinuousDistribution

In probability theory, a probability density function (pdf), or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point. The probability for the random variable to fall within a particular region is given by the integral of this variable’s density over the region. The probability density function is nonnegative everywhere, and its integral over the entire space is equal to one. WikipediA

Parameters:

aValue - x

Returns:

P(x)

getQuantile

public double getQuantile(double aProbality)

Description copied from interface: ContinuousDistribution

The quantile function, for any distribution, is defined for real variables between zero and one and is mathematically the inverse of the cumulative distribution function. WikipediA The input probability absolutely has to be [0.0, 1.0], but values close to 0.0 and 1.0 may be problematic

Parameters:

aProbality - P(<=x)

Returns:

x

getVariance

public double getVariance()

Description copied from class: RandomNumber

Subclasses must override either getStandardDeviation() or getVariance()!

Specified by:

getVariance in interface Distribution

Overrides:

getVariance in class RandomNumber

See Also:

Distribution.getStandardDeviation(), Distribution.getVariance()

getLowerConfidenceQuantile

public final double getLowerConfidenceQuantile(double aConfidence)

getUpperConfidenceQuantile

public final double getUpperConfidenceQuantile(double aConfidence)