BUILT-IN WOLFRAM LANGUAGE SYMBOL

# BirnbaumSaundersDistribution

represents the BirnbaumSaunders distribution with shape parameter α and scale parameter λ.

## Background & ContextBackground & Context

• represents a continuous statistical distribution defined over the interval and parametrized by two positive values α and λ. Here, α is known as a "shape parameter," γ is a so-called "scale parameter," and together these parameters determine various properties of the probability density function (PDF), including its height and its horizontal location in the plane. The PDF of the BirnbaumSaunders distribution is unimodal and has "thin tails" in the sense that the PDF decreases exponentially for large values . (This behavior can be made quantitatively precise by analyzing the SurvivalFunction of the distribution.)
• The BirnbaumSaunders distribution dates back to the late 1960s to the work of mathematicians Z. W. Birnbaum and S. C. Saunders and was originally proposed as a lifetime model for materials subject to cyclic patterns of stress and strain. Also known as the fatigue-life distribution, the BirnbaumSaunders distribution is still actively used to model life cycles in manufacturing. More recently, modified versions of the distribution have been used to accurately model the distribution of mineral concentration in drinking water. Elsewhere, the distribution has been used to approximate the quantile function of the inverse Gaussian distribution (InverseGaussianDistribution), to perform analyses in various areas of engineering science, and to model certain biological processes subject to rapid decline.
• RandomVariate can be used to give one or more machine- or arbitrary-precision (the latter via the WorkingPrecision option) pseudorandom variates from a BirnbaumSaunders distribution. Distributed[x,BirnbaumSaundersDistribution[α,λ]], written more concisely as xBirnbaumSaundersDistribution[α,λ], can be used to assert that a random variable x is distributed according to a BirnbaumSaunders 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[BirnbaumSaundersDistribution[α,λ],x] and CDF[BirnbaumSaundersDistribution[α,λ],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 BirnbaumSaunders distribution, EstimatedDistribution to estimate a BirnbaumSaunders parametric distribution from given data, and FindDistributionParameters to fit data to a BirnbaumSaunders distribution. ProbabilityPlot can be used to generate a plot of the CDF of given data against the CDF of a symbolic BirnbaumSaunders distribution and QuantilePlot to generate a plot of the quantiles of given data against the quantiles of a symbolic BirnbaumSaunders distribution.
• TransformedDistribution can be used to represent a transformed BirnbaumSaunders 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 BirnbaumSaunders distribution, and ProductDistribution can be used to compute a joint distribution with independent component distributions involving beta distributions.
• The BirnbaumSaunders distribution is related to a number of other distributions. For example, given a random variate , XBirnbaumSaundersDistribution[α,γ] if and only if YNormalDistribution[] where . Visually, the PDF of BirnbaumSaundersDistribution tends to appear "bell-shaped," thereby introducing qualitative relationships with a number of other distributions, including CauchyDistribution, StudentTDistribution, and LogisticDistribution. BirnbaumSaundersDistribution is also related to LogNormalDistribution, BetaDistribution, and JohnsonDistribution.

## ExamplesExamplesopen allclose all

### Basic Examples  (4)Basic Examples  (4)

Probability density function:

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Cumulative distribution function:

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Mean and variance:

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Median:

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## See AlsoSee Also

Introduced in 2010
(8.0)
| Updated in 2016
(10.4)