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prob.BirnbaumSaundersDistribution class

Package: prob
Superclasses: prob.ToolboxFittableParametricDistribution

Birnbaum-Saunders probability distribution object

Description

prob.BirnbaumSaundersDistribution is an object consisting of parameters, a model description, and sample data for a Birnbaum-Saunders probability distribution.

Create a probability distribution object with specified parameter values using makedist. Alternatively, fit a distribution to data using fitdist or the Distribution Fitting app.

Construction

pd = makedist('BirnbaumSaunders') creates a Birnbaum-Saunders probability distribution object using the default parameter values.

pd = makedist('BirnbaumSaunders','beta',beta,'gamma',gamma) creates a Birnbaum-Saunders distribution object using the specified parameter values.

Input Arguments

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beta — Scale parameter1 (default) | positive scalar value

Scale parameter of the Birnbaum-Saunders distribution, specified as a positive scalar value.

Data Types: single | double

gamma — Shape parameter1 (default) | nonnegative scalar value

Shape parameter of the Birnbaum-Saunders distribution, specified as a nonnegative scalar value.

Data Types: single | double

Properties

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beta — Scale parameterpositive scalar value

Scale parameter of the Birnbaum-Saunders distribution, stored as a positive scalar value.

Data Types: single | double

gamma — Shape parameternonnegative scalar value

Shape parameter of the Birnbaum-Saunders distribution, stored as a nonnegative scalar value.

Data Types: single | double

DistributionName — Probability distribution nameprobability distribution name string

Probability distribution name, stored as a valid probability distribution name string. This property is read-only.

Data Types: char

InputData — Data used for distribution fittingstructure

Data used for distribution fitting, stored as a structure containing the following:

  • data: Data vector used for distribution fitting.

  • cens: Censoring vector, or empty if none.

  • freq: Frequency vector, or empty if none.

This property is read-only.

Data Types: struct

IsTruncated — Logical flag for truncated distribution0 | 1

Logical flag for truncated distribution, stored as a logical value. If IsTruncated equals 0, the distribution is not truncated. If IsTruncated equals 1, the distribution is truncated. This property is read-only.

Data Types: logical

NumParameters — Number of parameterspositive integer value

Number of parameters for the probability distribution, stored as a positive integer value. This property is read-only.

Data Types: single | double

ParameterCovariance — Covariance matrix of the parameter estimatesmatrix of scalar values

Covariance matrix of the parameter estimates, stored as a p-by-p matrix, where p is the number of parameters in the distribution. The (i,j) element is the covariance between the estimates of the ith parameter and the jth parameter. The (i,i) element is the estimated variance of the ith parameter. If parameter i is fixed rather than estimated by fitting the distribution to data, then the (i,i) elements of the covariance matrix are 0. This property is read-only.

Data Types: single | double

ParameterDescription — Distribution parameter descriptionscell array of strings

Distribution parameter descriptions, stored as a cell array of strings. Each cell contains a short description of one distribution parameter. This property is read-only.

Data Types: char

ParameterIsFixed — Logical flag for fixed parametersarray of logical values

Logical flag for fixed parameters, stored as an array of logical values. If 0, the corresponding parameter in the ParameterNames array is not fixed. If 1, the corresponding parameter in the ParameterNames array is fixed. This property is read-only.

Data Types: logical

ParameterNames — Distribution parameter namescell array of strings

Distribution parameter names, stored as a cell array of strings. This property is read-only.

Data Types: char

ParameterValues — Distribution parameter valuesvector of scalar values

Distribution parameter values, stored as a vector. This property is read-only.

Data Types: single | double

Truncation — Truncation intervalvector of scalar values

Truncation interval for the probability distribution, stored as a vector containing the lower and upper truncation boundaries. This property is read-only.

Data Types: single | double

Methods

Inherited Methods

cdf Cumulative distribution function of probability distribution object
icdfInverse cumulative distribution function of probability distribution object
iqrInterquartile range of probability distribution object
median Median of probability distribution object
pdfProbability density function of probability distribution object
randomGenerate random numbers from probability distribution object
truncateTruncate probability distribution object
meanMean of probability distribution object
negloglikNegative loglikelihood of probability distribution object
paramciConfidence intervals for probability distribution parameters
proflikProfile likelihood function for probability distribution object
std Standard deviation of probability distribution object
varVariance of probability distribution object

Definitions

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. It is also called the fatigue life distribution.

The Birnbaum-Saunders distribution uses the following parameters.

ParameterDescriptionSupport
betaScale parameter
gammaShape parameter

The probability density function (pdf) is

Examples

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Create a Birnbaum-Saunders Distribution Object Using Default Parameters

Create a Birnbaum-Saunders distribution object using the default parameter values.

pd = makedist('BirnbaumSaunders')
pd = 

  BirnbaumSaundersDistribution

  Birnbaum-Saunders distribution
     beta = 1
    gamma = 1

Create a Birnbaum-Saunders Distribution Object Using Specified Parameter Values

Create a Birnbaum-Saunders distribution object by specifying the parameter values.

pd = makedist('BirnbaumSaunders','beta',2,'gamma',5)
pd = 

  BirnbaumSaundersDistribution

  Birnbaum-Saunders distribution
     beta = 2
    gamma = 5

Compute the mean of the distribution.

m = mean(pd)
m =

    27

See Also

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