Class NormalDistribution
- java.lang.Object
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- org.apache.commons.statistics.distribution.NormalDistribution
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- All Implemented Interfaces:
ContinuousDistribution
public class NormalDistribution extends java.lang.ObjectImplementation of the normal (Gaussian) distribution.
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Nested Class Summary
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Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.ContinuousDistribution
ContinuousDistribution.Sampler
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Constructor Summary
Constructors Constructor Description NormalDistribution(double mean, double sd)Creates a distribution.
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Method Summary
Modifier and Type Method Description ContinuousDistribution.SamplercreateSampler(UniformRandomProvider rng)Creates a sampler.doublecumulativeProbability(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.doublegetMean()Gets the mean of this distribution.doublegetStandardDeviation()Access the standard deviation.doublegetSupportLowerBound()Gets the lower bound of the support.doublegetSupportUpperBound()Gets the upper bound of the support.doublegetVariance()Gets the variance of this distribution.doubleinverseCumulativeProbability(double p)Computes the quantile function of this distribution.booleanisSupportConnected()Indicates whether the support is connected, i.e.doublelogDensity(double x)Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified pointx.doubleprobability(double x0, double x1)For a random variableXwhose values are distributed according to this distribution, this method returnsP(x0 < X <= x1).static double[]sample(int n, ContinuousDistribution.Sampler sampler)Utility function for allocating an array and filling it withnsamples generated by the givensampler.-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface org.apache.commons.statistics.distribution.ContinuousDistribution
probability
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Method Detail
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getStandardDeviation
public double getStandardDeviation()
Access the standard deviation.- Returns:
- the standard deviation for this distribution.
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density
public double density(double x)
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- Point at which the PDF is evaluated.- Returns:
- the value of the probability density function at
x.
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logDensity
public double logDensity(double x)
Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified pointx.- Parameters:
x- Point at which the PDF is evaluated.- Returns:
- the logarithm of the value of the probability density function
at
x.
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cumulativeProbability
public double cumulativeProbability(double x)
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. Ifxis more than 40 standard deviations from the mean, 0 or 1 is returned, as in these cases the actual value is withinDouble.MIN_VALUEof 0 or 1.- Parameters:
x- 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.
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inverseCumulativeProbability
public double inverseCumulativeProbability(double p)
Computes the quantile function of this distribution. For a random variableXdistributed according to this distribution, the returned value isinf{x in R | P(X<=x) >= p}for0 < p <= 1,inf{x in R | P(X<=x) > 0}forp = 0.
ContinuousDistribution.getSupportLowerBound()forp = 0,ContinuousDistribution.getSupportUpperBound()forp = 1.
- Specified by:
inverseCumulativeProbabilityin interfaceContinuousDistribution- Parameters:
p- Cumulative probability.- Returns:
- the smallest
p-quantile of this distribution (largest 0-quantile forp = 0).
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probability
public double probability(double x0, double x1)For a random variableXwhose values are distributed according to this distribution, this method returnsP(x0 < X <= x1). The default implementation uses the identityP(x0 < X <= x1) = P(X <= x1) - P(X <= x0)- Parameters:
x0- Lower bound (exclusive).x1- Upper bound (inclusive).- Returns:
- the probability that a random variable with this distribution
takes a value between
x0andx1, excluding the lower and including the upper endpoint.
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getMean
public double getMean()
Gets the mean of this distribution.- Returns:
- the mean, or
Double.NaNif it is not defined.
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getVariance
public double getVariance()
Gets the variance of this distribution. For standard deviation parameters, the variance iss^2.- Returns:
- the variance, or
Double.NaNif it is not defined.
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getSupportLowerBound
public double getSupportLowerBound()
Gets the lower bound of the support. It must return the same value asinverseCumulativeProbability(0), i.e.inf {x in R | P(X <= x) > 0}. The lower bound of the support is always negative infinity no matter the parameters.- Returns:
- lower bound of the support (always
Double.NEGATIVE_INFINITY)
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getSupportUpperBound
public double getSupportUpperBound()
Gets the upper bound of the support. It must return the same value asinverseCumulativeProbability(1), i.e.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)
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isSupportConnected
public boolean isSupportConnected()
Indicates 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
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createSampler
public ContinuousDistribution.Sampler createSampler(UniformRandomProvider rng)
Creates a sampler.- Specified by:
createSamplerin interfaceContinuousDistribution- Parameters:
rng- Generator of uniformly distributed numbers.- Returns:
- a sampler that produces random numbers according this distribution.
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sample
public static double[] sample(int n, ContinuousDistribution.Sampler sampler)Utility function for allocating an array and filling it withnsamples generated by the givensampler.- Parameters:
n- Number of samples.sampler- Sampler.- Returns:
- an array of size
n.
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