gov.sandia.cognition.statistics.distribution
Class LogNormalDistribution

java.lang.Object
  gov.sandia.cognition.util.AbstractCloneableSerializable
      gov.sandia.cognition.statistics.AbstractDistribution<NumberType>
          gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<Double>
              gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
                  gov.sandia.cognition.statistics.distribution.LogNormalDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<Double>, ClosedFormDistribution<Double>, ClosedFormUnivariateDistribution<Double>, ComputableDistribution<Double>, Distribution<Double>, DistributionWithMean<Double>, EstimableDistribution<Double,LogNormalDistribution>, SmoothUnivariateDistribution, UnivariateDistribution<Double>, CloneableSerializable, Serializable, Cloneable

Direct Known Subclasses:

LogNormalDistribution.CDF, LogNormalDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Log-normal distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Log-normal_distribution")
public class LogNormalDistribution
extends AbstractClosedFormSmoothUnivariateDistribution
implements EstimableDistribution<Double,LogNormalDistribution>

Log-Normal distribution PDF and CDF implementations. The Log-Normal distribution is related to a UnivariateGaussian where the natural logarithm of the random variable is normally distributed. This turns up in application areas that are the product of some random variables, where each random variable is i.i.d. and normally distributed. Stock market returns are the classic example of a Log-Normal distribution.

Since:

2.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

static class	LogNormalDistribution.CDF CDF of the Log-Normal Distribution
static class	LogNormalDistribution.MaximumLikelihoodEstimator Maximum Likelihood Estimator of a log-normal distribution.
static class	LogNormalDistribution.PDF PDF of a Log-normal distribution
static class	LogNormalDistribution.WeightedMaximumLikelihoodEstimator Maximum Likelihood Estimator from weighted data

Field Summary

static double	DEFAULT_LOG_NORMAL_MEAN Default log normal mean, 0.0.
static double	DEFAULT_LOG_NORMAL_VARIANCE Default log normal variance, 1.0.
static double	SQRT2PI Constant value of Math.sqrt(2*Math.PI)

Constructor Summary

LogNormalDistribution()
Default constructor.

LogNormalDistribution(double logNormalMean, double logNormalVariance)
Creates a new instance of LogNormalDistribution

LogNormalDistribution(LogNormalDistribution other)
Copy Constructor

Method Summary

void	convertFromVector(Vector parameters) Sets the parameters of the distribution from a 2-dimensional Vector with ( logNormalMean logNormalVariance )
Vector	convertToVector() Returns a 2-dimensional Vector with ( logNormalMean logNormalVariance )
LogNormalDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
LogNormalDistribution.MaximumLikelihoodEstimator	getEstimator() Gets an estimator associated with this distribution.
double	getLogNormalMean() Getter for logNormalMean
double	getLogNormalVariance() Getter for logNormalVariance
Double	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
Double	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
Double	getMinSupport() Gets the minimum support (domain or input) of the distribution.
LogNormalDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getVariance() Gets the variance of the distribution.
ArrayList<Double>	sample(Random random, int numSamples) Draws multiple random samples from the distribution.
void	setLogNormalMean(double logNormalMean) Setter for logNormalMean
void	setLogNormalVariance(double logNormalVariance) Setter for logNormalVariance
String	toString()

Methods inherited from class gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution

clone

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Methods inherited from interface gov.sandia.cognition.math.matrix.Vectorizable

clone

Field Detail

DEFAULT_LOG_NORMAL_MEAN

public static final double DEFAULT_LOG_NORMAL_MEAN

Default log normal mean, 0.0.

See Also:

Constant Field Values

DEFAULT_LOG_NORMAL_VARIANCE

public static final double DEFAULT_LOG_NORMAL_VARIANCE

Default log normal variance, 1.0.

See Also:

Constant Field Values

SQRT2PI

public static final double SQRT2PI

Constant value of Math.sqrt(2*Math.PI)

Constructor Detail

LogNormalDistribution

public LogNormalDistribution()

Default constructor.

LogNormalDistribution

public LogNormalDistribution(double logNormalMean,
                             double logNormalVariance)

Creates a new instance of LogNormalDistribution

Parameters:

logNormalMean - Mean of the underlying distribution, (-infinity,+infinity)

logNormalVariance - Variance of the underlying distribution, (0,infinity)

LogNormalDistribution

public LogNormalDistribution(LogNormalDistribution other)

Copy Constructor

Parameters:

other - LogNormalDistribution to copy

Method Detail

convertToVector

public Vector convertToVector()

Returns a 2-dimensional Vector with ( logNormalMean logNormalVariance )

Specified by:

convertToVector in interface Vectorizable

Returns:

2-dimensional Vector with ( logNormalMean logNormalVariance )

convertFromVector

public void convertFromVector(Vector parameters)

Sets the parameters of the distribution from a 2-dimensional Vector with ( logNormalMean logNormalVariance )

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - 2-dimensional Vector with ( logNormalMean logNormalVariance )

getLogNormalMean

public double getLogNormalMean()

Getter for logNormalMean

Returns:

Mean of the underlying distribution, (-infinity,+infinity)

setLogNormalMean

public void setLogNormalMean(double logNormalMean)

Setter for logNormalMean

Parameters:

logNormalMean - Mean of the underlying distribution, (-infinity,+infinity)

getLogNormalVariance

public double getLogNormalVariance()

Getter for logNormalVariance

Returns:

Variance of the underlying distribution, (0,infinity)

setLogNormalVariance

public void setLogNormalVariance(double logNormalVariance)

Setter for logNormalVariance

Parameters:

logNormalVariance - Variance of the underlying distribution, (0,infinity)

getMean

public Double getMean()

Description copied from interface: DistributionWithMean

Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Specified by:

getMean in interface DistributionWithMean<Double>

Specified by:

getMean in interface SmoothUnivariateDistribution

Returns:

Mean of the distribution.

getVariance

public double getVariance()

Description copied from interface: UnivariateDistribution

Gets the variance of the distribution. This is sometimes called the second central moment by more pedantic people, which is equivalent to the square of the standard deviation.

Specified by:

getVariance in interface UnivariateDistribution<Double>

Returns:

Variance of the distribution.

sample

public ArrayList<Double> sample(Random random,
                                int numSamples)

Description copied from interface: Distribution

Draws multiple random samples from the distribution. It is generally more efficient to use this multiple-sample method than multiple calls of the single-sample method. (But not always.)

Specified by:

sample in interface Distribution<Double>

Parameters:

random - Random-number generator to use in order to generate random numbers.

numSamples - Number of samples to draw from the distribution.

Returns:

Samples drawn according to this distribution.

getCDF

public LogNormalDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<Double>

Specified by:

getCDF in interface SmoothUnivariateDistribution

Specified by:

getCDF in interface UnivariateDistribution<Double>

Returns:

CDF of the scalar distribution.

getProbabilityFunction

public LogNormalDistribution.PDF getProbabilityFunction()

Description copied from interface: ComputableDistribution

Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:

getProbabilityFunction in interface ComputableDistribution<Double>

Specified by:

getProbabilityFunction in interface SmoothUnivariateDistribution

Returns:

Distribution function associated with this Distribution.

toString

public String toString()

Overrides:

toString in class Object

getMinSupport

public Double getMinSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMinSupport in interface UnivariateDistribution<Double>

Returns:

Minimum support.

getMaxSupport

public Double getMaxSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMaxSupport in interface UnivariateDistribution<Double>

Returns:

Minimum support.

getEstimator

public LogNormalDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

getEstimator in interface EstimableDistribution<Double,LogNormalDistribution>

Returns:

A distribution estimator associated for this distribution.