Contents

prob.InverseGaussianDistribution class

Package: prob
Superclasses: prob.ToolboxFittableParametricDistribution

Inverse Gaussian probability distribution object

Description

prob.InverseGaussianDistribution is an object consisting of parameters, a model description, and sample data for an inverse Gaussian 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('InverseGaussian') creates an inverse Gaussian probability distribution object using the default parameter values.

pd = makedist('InverseGaussian','mu',mu,'lambda',lambda) creates an inverse Gaussian probability distribution object using the specified parameter values.

Input Arguments

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mu — Scale parameter1 (default) | positive scalar value

Scale parameter for the inverse Gaussian distribution, specified as a positive scalar value.

Data Types: single | double

lambda — Shape parameter1 (default) | positive scalar value

Shape parameter for the inverse Gaussian distribution, specified as a positive scalar value.

Data Types: single | double

Properties

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muScale parameterpositive scalar value

Scale parameter for the inverse Gaussian distribution, stored as a positive scalar value.

Data Types: single | double

lambdaShape parameterpositive scalar value

Shape parameter for the inverse Gaussian distribution, stored as a positive scalar value.

Data Types: single | double

DistributionNameProbability distribution nameprobability distribution name string

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

Data Types: char

InputDataData 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

IsTruncatedLogical 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

NumParametersNumber 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

ParameterCovarianceCovariance 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

ParameterDescriptionDistribution 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

ParameterIsFixedLogical 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

ParameterNamesDistribution parameter namescell array of strings

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

Data Types: char

ParameterValuesDistribution parameter valuesvector of scalar values

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

Data Types: single | double

TruncationTruncation 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

Inverse Gaussian Distribution

Also known as the Wald distribution, the inverse Gaussian is used to model nonnegative positively skewed data. Inverse Gaussian distributions have many similarities to standard Gaussian (normal) distributions, which lead to applications in inferential statistics.

The inverse Gaussian distribution uses the following parameters.

ParameterDescriptionSupport
muScale parameterμ>0
lambdaShape parameterλ>0

The probability density function (pdf) is

f(x|μ,λ)=λ2πx3exp{λ2μ2x(xμ)2};x>0.

Examples

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Create an Inverse Gaussian Distribution Object Using Default Parameters

Create an inverse Gaussian distribution object using the default parameter values.

pd = makedist('InverseGaussian')
pd = 

  InverseGaussianDistribution

  Inverse Gaussian distribution
        mu = 1
    lambda = 1

Create an Inverse Gaussian Distribution Object Using Specified Parameters

Create an inverse Gaussian distribution object by specifying parameter values.

pd = makedist('InverseGaussian','mu',2,'lambda',4)
pd = 

  InverseGaussianDistribution

  Inverse Gaussian distribution
        mu = 2
    lambda = 4

Compute the standard deviation of the distribution.

s = std(pd)
s =

    1.4142
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