# Documentation

### This is machine translation

Translated by
Mouseover text to see original. Click the button below to return to the English verison of the page.

# 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`.

## Compatibility

`ProbDistUnivParam` will be removed in a future release. To create and fit probability distribution objects, use `makedist` and `fitdist` instead.

## Input Arguments

 `DistName` A character vector 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.