# Birnbaum-Saunders Distribution

Fit, evaluate, and generate random samples from Birnbaum-Saunders distribution

Statistics and Machine Learning Toolbox™ offers multiple ways to work with the Birnbaum-Saunders distribution.

• Create a `BirnbaumSaundersDistribution` object and use `BirnbaumSaundersDistribution` object functions.

• Use the generic distribution functions with the specified distribution name `"BirnbaumSaunders"` and corresponding parameters.

To learn about the Birnbaum-Saunders distribution, see Birnbaum-Saunders Distribution.

## Functions

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 `makedist` Create probability distribution object `fitdist` Fit probability distribution object to data `distributionFitter` Open Distribution Fitter app
 `cdf` Cumulative distribution function `gather` Gather properties of Statistics and Machine Learning Toolbox object from GPU (Since R2020b) `icdf` Inverse cumulative distribution function `iqr` Interquartile range of probability distribution `mean` Mean of probability distribution `median` Median of probability distribution `negloglik` Negative loglikelihood of probability distribution `paramci` Confidence intervals for probability distribution parameters `pdf` Probability density function `plot` Plot probability distribution object (Since R2022b) `proflik` Profile likelihood function for probability distribution `random` Random numbers `std` Standard deviation of probability distribution `truncate` Truncate probability distribution object `var` Variance of probability distribution
 `cdf` Cumulative distribution function `icdf` Inverse cumulative distribution function `pdf` Probability density function `random` Random numbers `mle` Maximum likelihood estimates

## Objects

 `BirnbaumSaundersDistribution` Birnbaum-Saunders probability distribution object

## Topics

• Birnbaum-Saunders Distribution

The Birnbaum-Saunders distribution was originally proposed as a lifetime model for materials subject to cyclic patterns of stress and strain, where the ultimate failure of the material comes from the growth of a prominent flaw.