makedist

Create probability distribution object

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Syntax

pd = makedist(distname)
pd = makedist(distname,Name,Value)
list = makedist
makedist -reset

Description

example

pd = makedist(distname) creates a probability distribution object for the distribution distname, using the default parameter values.

example

pd = makedist(distname,Name,Value) creates a probability distribution object with one or more distribution parameter values specified by name-value pair arguments.

list = makedist returns a cell array list containing a list of the probability distributions that makedist can create.

makedist -reset resets the list of distributions by searching the path for files contained in a package named prob and implementing classes derived from ProbabilityDistribution. Use this syntax after you define a custom distribution function. For details, see Define Custom Distributions Using the Distribution Fitter App.

Examples

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Open Live Script

Create a normal distribution object using the default parameter values. 

pd = makedist('Normal')
pd = 
  NormalDistribution

  Normal distribution
       mu = 0
    sigma = 1

Compute the interquartile range of the distribution. 

r = iqr(pd)
r = 1.3490
Open Live Script

Create a gamma distribution object using the default parameter values. 

pd = makedist('Gamma')
pd = 
  GammaDistribution

  Gamma distribution
    a = 1
    b = 1

Compute the mean of the gamma distribution. 

mean = mean(pd)
mean = 1
Open Live Script

Create a normal distribution object with parameter values mu = 75 and sigma = 10.

pd = makedist('Normal','mu',75,'sigma',10)
pd = 
  NormalDistribution

  Normal distribution
       mu = 75
    sigma = 10
Open Live Script

Create a gamma distribution object with the parameter value a = 3 and the default value b = 1. 

pd = makedist('Gamma','a',3)
pd = 
  GammaDistribution

  Gamma distribution
    a = 3
    b = 1

Input Arguments

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Distribution name, specified as one of the following character vectors or string scalars. The distribution specified by distname determines the type of the returned probability distribution object.

Distribution NameDescriptionDistribution Object
'Beta'Beta distributionBetaDistribution
'Binomial'Binomial distributionBinomialDistribution
'BirnbaumSaunders'Birnbaum-Saunders distributionBirnbaumSaundersDistribution
'Burr'Burr distributionBurrDistribution
'Exponential'Exponential distributionExponentialDistribution
'ExtremeValue'Extreme Value distributionExtremeValueDistribution
'Gamma'Gamma distributionGammaDistribution
'GeneralizedExtremeValue'Generalized Extreme Value distributionGeneralizedExtremeValueDistribution
'GeneralizedPareto'Generalized Pareto distributionGeneralizedParetoDistribution
'HalfNormal'Half-normal distributionHalfNormalDistribution
'InverseGaussian'Inverse Gaussian distributionInverseGaussianDistribution
'Logistic'Logistic distributionLogisticDistribution
'Loglogistic'Loglogistic distributionLoglogisticDistribution
'Lognormal'Lognormal distributionLognormalDistribution
'Multinomial'Multinomial distributionMultinomialDistribution
'Nakagami'Nakagami distributionNakagamiDistribution
'NegativeBinomial'Negative Binomial distributionNegativeBinomialDistribution
'Normal'Normal distributionNormalDistribution
'PiecewiseLinear'Piecewise Linear distributionPiecewiseLinearDistribution
'Poisson'Poisson distributionPoissonDistribution
'Rayleigh'Rayleigh distributionRayleighDistribution
'Rician'Rician distributionRicianDistribution
'Stable'Stable distributionStableDistribution
'tLocationScale't Location-Scale distributiontLocationScaleDistribution
'Triangular'Triangular distributionTriangularDistribution
'Uniform'Uniform distributionUniformDistribution
'Weibull'Weibull distributionWeibullDistribution

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: makedist('Normal','mu',10) specifies a normal distribution with parameter mu equal to 10, and parameter sigma equal to the default value of 1.
Beta Distribution

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Example: 'a',3

Data Types: single | double

Example: 'b',5

Data Types: single | double

Binomial Distribution

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Example: 'N',25

Data Types: single | double

Example: 'p',0.25

Data Types: single | double

Birnbaum-Saunders Distribution

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Example: 'beta',2

Data Types: single | double

Example: 'gamma',0

Data Types: single | double

Burr Distribution

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Example: 'alpha',2

Data Types: single | double

Example: 'c',2

Data Types: single | double

Example: 'k',5

Data Types: single | double

Exponential Distribution

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Example: 'mu',5

Data Types: single | double

Extreme Value Distribution

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Example: 'mu',-2

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Gamma Distribution

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Example: 'a',2

Data Types: single | double

Example: 'b',0

Data Types: single | double

Generalized Extreme Value Distribution

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Example: 'k',0

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Example: 'mu',1

Data Types: single | double

Generalized Pareto Distribution

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Example: 'k',0

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Example: 'theta',2

Data Types: single | double

Half–Normal Distribution

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Example: 'mu',1

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Inverse Gaussian Distribution

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Example: 'mu',2

Data Types: single | double

Example: 'lambda',4

Data Types: single | double

Logistic Distribution

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Example: 'mu',2

Data Types: single | double

Example: 'sigma',4

Data Types: single | double

Loglogistic Distribution

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Example: 'mu',2

Data Types: single | double

Example: 'sigma',4

Data Types: single | double

Lognormal Distribution

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Example: 'mu',2

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Multinomial Distribution

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Outcome probabilities, specified as a vector of scalar values in the range [0,1]. The probabilities sum to 1 and correspond to outcomes [1, 2, ..., k], where k is the number of elements in the probabilities vector.

Example: 'probabilities',[0.1 0.2 0.5 0.2] gives the probabilities that the outcome is 1, 2, 3, or 4, respectively.

Data Types: single | double

Nakagami Distribution

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Example: 'mu',5

Data Types: single | double

Example: 'omega',5

Data Types: single | double

Negative Binomial Distribution

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Example: 'R',5

Data Types: single | double

Example: 'p',0.1

Data Types: single | double

Normal Distribution

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Example: 'mu',2

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Piecewise Linear Distribution

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Example: 'x',[1 2 3]

Data Types: single | double

Example: 'Fx',[0.2 0.5 1]

Data Types: single | double

Poisson Distribution

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Example: 'lambda',5

Data Types: single | double

Rayleigh Distribution

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Example: 'b',3

Data Types: single | double

Rician Distribution

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Example: 's',0

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Stable Distribution

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Example: 'alpha',1

Data Types: single | double

Example: 'beta',0.5

Data Types: single | double

Example: 'gam',2

Data Types: single | double

Example: 'delta',5

Data Types: single | double

t Location-Scale Distribution

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Example: 'mu',-2

Data Types: single | double

Example: 'sigma',2

Data Types: single | double

Example: 'nu',20

Data Types: single | double

Triangular Distribution

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Example: 'a',-2

Data Types: single | double

Example: 'b',1

Data Types: single | double

Example: 'c',5

Data Types: single | double

Uniform Distribution

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Example: 'lower',-4

Data Types: single | double

Example: 'upper',2

Data Types: single | double

Weibull Distribution

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Example: 'a',2

Data Types: single | double

Example: 'b',5

Data Types: single | double

Output Arguments

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Probability distribution, returned as a probability distribution object of the type specified by distname.

List of probability distributions that makedist can create, returned as a cell array of character vectors.

Alternative Functionality

App

The Distribution Fitter app opens a graphical user interface for you to import data from the workspace and interactively fit a probability distribution to that data. You can then save the distribution to the workspace as a probability distribution object. Open the Distribution Fitter app using distributionFitter, or click Distribution Fitter on the Apps tab.

See Also

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Topics

Introduced in R2013a

Statistics and Machine Learning Toolbox Documentation

Support

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

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