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HalfNormalDistribution—Wolfram Language Documentation

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• See Also
  • NormalDistribution
  • ChiDistribution
  • Erf
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  • Normal and Related Distributions
  • Distributions Used in Actuarial Science
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  • Continuous Distributions

HalfNormalDistribution[θ]

represents a half-normal distribution with scale inversely proportional to parameter θ.

• HalfNormalDistribution[θ] represents a continuous statistical distribution defined on the interval and parametrized by a positive real number θ that determines the overall height and steepness of the associated probability density function (PDF). The PDF of the half-normal distribution is smooth and monotonically decreasing with tails that are "thin," in the sense that the PDF decreases exponentially for large values of . (This behavior can be made quantitatively precise by analyzing the SurvivalFunction of the distribution.) The half-normal distribution may be referred to as a folded or a doubly truncated normal distribution (see NormalDistribution), both of which are references to generalizations of NormalDistribution that can be formalized in a way that yields HalfNormalDistribution as a special case.
• The half-normal distribution has a sparse but colorful past dating back to the late 1940s, though it failed to become the focal point for considerable research until the 1960s. Derived as a special case of NormalDistribution in which the sign of the random variate is always positive, the half-normal distribution has been used to study various phenomena, including deviations in automotive part manufacturing, and has been utilized as a modeling tool in areas such as economics, industry, physiology, and quality control. The half-normal distribution has also been used in Bayesian statistics as a prior distribution for the standard deviations of certain distributions.
• RandomVariate can be used to give one or more machine- or arbitrary-precision (the latter via the WorkingPrecision option) pseudorandom variates from a half-normal distribution. Distributed[x,HalfNormalDistribution[θ]], written more concisely as xHalfNormalDistribution[θ], can be used to assert that a random variable x is distributed according to a half-normal distribution. Such an assertion can then be used in functions such as Probability, NProbability, Expectation, and NExpectation.
• The probability density and cumulative distribution functions may be given using PDF[HalfNormalDistribution[θ],x] and CDF[HalfNormalDistribution[θ],x]. The mean, median, variance, raw moments, and central moments may be computed using Mean, Median, Variance, Moment, and CentralMoment, respectively.
• DistributionFitTest can be used to test if a given dataset is consistent with a half-normal distribution, EstimatedDistribution to estimate a half-normal parametric distribution from given data, and FindDistributionParameters to fit data to a half-normal distribution. ProbabilityPlot can be used to generate a plot of the CDF of given data against the CDF of a symbolic half-normal distribution and QuantilePlot to generate a plot of the quantiles of given data against the quantiles of a symbolic half-normal distribution.
• TransformedDistribution can be used to represent a transformed half-normal distribution, CensoredDistribution to represent the distribution of values censored between upper and lower values, and TruncatedDistribution to represent the distribution of values truncated between upper and lower values. CopulaDistribution can be used to build higher-dimensional distributions that contain a half-normal distribution, and ProductDistribution can be used to compute a joint distribution with independent component distributions involving half-normal distributions.
• HalfNormalDistribution is closely related to a number of other distributions. For example, HalfNormalDistribution[θ] can be viewed as both a truncation and a transformation of NormalDistribution, in the sense that its PDF is precisely the same as both TruncatedDistribution[{0,∞},NormalDistribution[0,]] and TransformedDistribution[Abs[x-μ],xNormalDistribution[μ,/θ]]. HalfNormalDistribution is also a special case of both GammaDistribution (the PDF of GammaDistribution[1/2,β,2,0] is the same as that of HalfNormalDistribution[/β]) and NakagamiDistribution (the PDF of NakagamiDistribution[1/2,π/(2 θ²)] is precisely that of HalfNormalDistribution[θ]), and HalfNormalDistribution[θ] is a limiting case of SkewNormalDistribution[0,/(θ),α] as α tends to Infinity. HalfNormalDistribution is also related to PearsonDistribution, ChiDistribution, ChiSquareDistribution, StudentTDistribution, and HoytDistribution.

Probability density function:

Cumulative distribution function:

Mean and variance of a half-normal distribution:

Median:

Generate a sample of pseudorandom numbers from a half-normal distribution:

Compare its histogram to the PDF:

Distribution parameters estimation:

Estimate the distribution parameters from sample data:

Compare the density histogram of the sample with the PDF of the estimated distribution:

Skewness and kurtosis are constant:

Different moments with closed forms as functions of parameters:

Moment:

Closed form for symbolic order:

CentralMoment:

FactorialMoment:

Cumulant:

Hazard function:

Quantile function:

Consistent use of Quantity in parameters yields QuantityDistribution:

Find typical spread of times:

Compute the -value for a -test of a random variate under the null hypothesis with alternative hypothesis :

Compare with Probability:

Measurement errors are independent and follow a centered normal distribution with standard deviation of 0.1 seconds. Find the distribution of the absolute errors:

Probability density function:

Find the average absolute error:

Find the probability that the absolute error is greater than 0.2 seconds:

Simulate absolute errors for the next 100 measurements:

HalfNormalDistribution is not defined when θ is not a positive real number:

Substitution of invalid parameters into symbolic outputs gives results that are not meaningful:

PDFs for different θ values with CDF contours:

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