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

`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

BirnbaumSaundersDistribution Birnbaum-Saunders probability distribution object

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.