org.apache.commons.math4.distribution

Class LogNormalDistribution

• All Implemented Interfaces:
Serializable, RealDistribution

public class LogNormalDistribution
extends AbstractRealDistribution
Implementation of the log-normal (gaussian) distribution.

Parameters: X is log-normally distributed if its natural logarithm log(X) is normally distributed. The probability distribution function of X is given by (for x > 0)

exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) * x)

• m is the scale parameter: this is the mean of the normally distributed natural logarithm of this distribution,
• s is the shape parameter: this is the standard deviation of the normally distributed natural logarithm of this distribution.
Since:
3.0
Log-normal distribution (Wikipedia), Log Normal distribution (MathWorld), Serialized Form

• Nested classes/interfaces inherited from interface org.apache.commons.math4.distribution.RealDistribution

RealDistribution.Sampler
• Field Summary

Fields
Modifier and Type Field and Description
static double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.
• Fields inherited from class org.apache.commons.math4.distribution.AbstractRealDistribution

SOLVER_DEFAULT_ABSOLUTE_ACCURACY
• Constructor Summary

Constructors
Constructor and Description
LogNormalDistribution()
Creates a log-normal distribution, where the mean and standard deviation of the normally distributed natural logarithm of the log-normal distribution are equal to zero and one respectively.
LogNormalDistribution(double scale, double shape)
Creates a log-normal distribution.
LogNormalDistribution(double scale, double shape, double inverseCumAccuracy)
Creates a log-normal distribution.
• Method Summary

All Methods
Modifier and Type Method and Description
RealDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
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 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.
double logDensity(double x)
Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point 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).
• Methods inherited from class org.apache.commons.math4.distribution.AbstractRealDistribution

inverseCumulativeProbability, probability, sample
• Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• Field Detail

• DEFAULT_INVERSE_ABSOLUTE_ACCURACY

public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.
Constant Field Values
• Constructor Detail

• LogNormalDistribution

public LogNormalDistribution()
Creates a log-normal distribution, where the mean and standard deviation of the normally distributed natural logarithm of the log-normal distribution are equal to zero and one respectively. In other words, the scale of the returned distribution is 0, while its shape is 1.
• LogNormalDistribution

public LogNormalDistribution(double scale,
double shape)
throws NotStrictlyPositiveException
Creates a log-normal distribution.
Parameters:
scale - Scale parameter of this distribution.
shape - Shape parameter of this distribution.
Throws:
NotStrictlyPositiveException - if shape <= 0.
• LogNormalDistribution

public LogNormalDistribution(double scale,
double shape,
double inverseCumAccuracy)
throws NotStrictlyPositiveException
Creates a log-normal distribution.
Parameters:
scale - Scale parameter of this distribution.
shape - Shape parameter of this distribution.
inverseCumAccuracy - Inverse cumulative probability accuracy.
Throws:
NotStrictlyPositiveException - if shape <= 0.
• Method Detail

• getScale

public double getScale()
Returns the scale parameter of this distribution.
Returns:
the scale parameter
• getShape

public double getShape()
Returns the shape parameter of this distribution.
Returns:
the shape parameter
• density

public 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. For scale m, and shape s of this distribution, the PDF is given by
• 0 if x <= 0,
• exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) * x) otherwise.
Parameters:
x - the point at which the PDF is evaluated
Returns:
the value of the probability density function at point x
• logDensity

public double logDensity(double x)
Returns the natural logarithm of 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. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of RealDistribution.density(double).

The default implementation simply computes the logarithm of density(x). See documentation of density(double) for computation details.

Specified by:
logDensity in interface RealDistribution
Overrides:
logDensity in class AbstractRealDistribution
Parameters:
x - the point at which the PDF is evaluated
Returns:
the logarithm of the value of the probability density function at point x
• cumulativeProbability

public 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. For scale m, and shape s of this distribution, the CDF is given by
• 0 if x <= 0,
• 0 if ln(x) - m < 0 and m - ln(x) > 40 * s, as in these cases the actual value is within Double.MIN_VALUE of 0,
• 1 if ln(x) - m >= 0 and ln(x) - m > 40 * s, as in these cases the actual value is within Double.MIN_VALUE of 1,
• 0.5 + 0.5 * erf((ln(x) - m) / (s * sqrt(2)) otherwise.
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
• probability

public double probability(double x0,
double x1)
throws NumberIsTooLargeException
For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
Specified by:
probability in interface RealDistribution
Overrides:
probability in class AbstractRealDistribution
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)
• getSolverAbsoluteAccuracy

protected double 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:
getSolverAbsoluteAccuracy in class AbstractRealDistribution
Returns:
the maximum absolute error in inverse cumulative probability estimates
• getNumericalMean

public double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution. For scale m and shape s, the mean is exp(m + s^2 / 2).
Returns:
the mean or Double.NaN if it is not defined
• getNumericalVariance

public double getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution. For scale m and shape s, the variance is (exp(s^2) - 1) * exp(2 * m + s^2).
Returns:
the variance (possibly Double.POSITIVE_INFINITY as for certain cases in TDistribution) or Double.NaN if it is not defined
• getSupportLowerBound

public 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}.

The lower bound of the support is always 0 no matter the parameters.
Returns:
lower bound of the support (always 0)
• getSupportUpperBound

public 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}.

The upper bound of the support is always positive infinity no matter the parameters.
Returns:
upper bound of the support (always Double.POSITIVE_INFINITY)
• isSupportConnected

public 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. The support of this distribution is connected.
Returns:
true
• createSampler

public RealDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
Specified by:
createSampler in interface RealDistribution
Overrides:
createSampler in class AbstractRealDistribution
Parameters:
rng - Generator of uniformly distributed numbers.
Returns:
a sampler that produces random numbers according this distribution.