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ProbDistUnivParam

Class: ProbDistUnivParam

Construct ProbDistUnivParam object

Syntax

PD = ProbDistUnivParam(DistName, Params)

Description

PD = ProbDistUnivParam(DistName, Params) creates PD, a ProbDistUnivParam object, which represents a probability distribution. This distribution is defined by the parametric distribution specified by DistName, with parameters specified by the numeric vector Params.

Input Arguments

DistName

A string specifying a distribution. Choices are:

  • 'beta'

  • 'binomial'

  • 'birnbaumsaunders'

  • 'exponential'

  • 'extreme value' or ev'

  • 'gamma'

  • 'generalized extreme value' or 'gev'

  • 'generalized pareto' or 'gp'

  • 'inversegaussian'

  • 'logistic'

  • 'loglogistic'

  • 'lognormal'

  • 'nakagami'

  • 'negative binomial' or 'nbin'

  • 'normal'

  • 'poisson'

  • 'rayleigh'

  • 'rician'

  • 'tlocationscale'

  • 'weibull' or 'wbl'

For more information on these parametric distributions, see Distribution Reference.

Params

Numeric vector of distribution parameters. The number and type of parameters depends on the distribution you specify with DistName. For information on parameters for each distribution type, see Distribution Reference.

Output Arguments

PD

An object in the ProbDistUnivParam class, which is derived from the ProbDist class. It represents a parametric probability distribution.

Examples

  1. Create an object representing a normal distribution with a mean of 100 and a standard deviation of 10.

    pd = ProbDistUnivParam('normal',[100 10])
    
    pd = 
    
    normal distribution
    
        mu = 100
        sigma = 10
    
  2. Generate a 4-by-5 matrix of random values from this distribution.

    random(pd,4,5)
    
    ans =
    
      105.3767  103.1877  135.7840  107.2540   98.7586
      118.3389   86.9231  127.6944   99.3695  114.8970
       77.4115   95.6641   86.5011  107.1474  114.0903
      108.6217  103.4262  130.3492   97.9503  114.1719

References

[1] Johnson, N. L., S. Kotz, and N. Balakrishnan. Continuous Univariate Distributions. Vol. 1, Hoboken, NJ: Wiley-Interscience, 1993.

[2] Johnson, N. L., S. Kotz, and N. Balakrishnan. Continuous Univariate Distributions. Vol. 2, Hoboken, NJ: Wiley-Interscience, 1994.

See Also

How To

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