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# icdf

Inverse cumulative distribution function

## Syntax

``x = icdf(name,p,A)``
``x = icdf(name,p,A,B)``
``x = icdf(name,p,A,B,C)``
``x = icdf(name,p,A,B,C,D)``
``x = icdf(pd,p)``

## Description

````x = icdf(name,p,A)` returns the inverse cumulative distribution function (icdf) for the one-parameter distribution family specified by `name` and the distribution parameter `A`, evaluated at the probability values in `p`.```

example

````x = icdf(name,p,A,B)` returns the icdf for the two-parameter distribution family specified by `name` and the distribution parameters `A` and `B`, evaluated at the probability values in `p`.```
````x = icdf(name,p,A,B,C)` returns the icdf for the three-parameter distribution family specified by `name` and the distribution parameters `A`, `B`, and `C`, evaluated at the probability values in `p`.```
````x = icdf(name,p,A,B,C,D)` returns the icdf for the four-parameter distribution family specified by `name` and the distribution parameters `A`, `B`, `C`, and `D`, evaluated at the probability values in `p`.```

example

````x = icdf(pd,p)` returns the icdf function of the probability distribution object `pd`, evaluated at the probability values in `p`.```

## Examples

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Compute the icdf values for a normal distribution by specifying the distribution name `'Normal'` and the distribution parameters.

Define the input vector p to contain the probability values at which to calculate the icdf.

`p = [0.1,0.25,0.5,0.75,0.9];`

Compute the icdf values for the normal distribution with the mean $\mu$ equal to 1 and the standard deviation $\sigma$ equal to 5.

```mu = 1; sigma = 5; y = icdf('Normal',p,mu,sigma)```
```y = 1×5 -5.4078 -2.3724 1.0000 4.3724 7.4078 ```

Each value in y corresponds to a value in the input vector x. For example, at the value x equal to 1, the corresponding icdf value y is equal to 7.4078.

Create a normal distribution object and compute the icdf values of the normal distribution using the object.

Create a normal distribution object with the mean $\mu$ equal to 1 and the standard deviation $\sigma$ equal to 5.

```mu = 1; sigma = 5; pd = makedist('Normal','mu',mu,'sigma',sigma);```

Define the input vector p to contain the probability values at which to calculate the icdf.

`p = [0.1,0.25,0.5,0.75,0.9];`

Compute the icdf values for the normal distribution at the values in p.

`x = icdf(pd,p)`
```x = 1×5 -5.4078 -2.3724 1.0000 4.3724 7.4078 ```

Each value in x corresponds to a value in the input vector p. For example, at the value p equal to 0.9, the corresponding icdf value x is equal to 7.4078.

Create a Poisson distribution object with the rate parameter, $\lambda$, equal to 2.

```lambda = 2; pd = makedist('Poisson','lambda',lambda);```

Define the input vector p to contain the probability values at which to calculate the icdf.

`p = [0.1,0.25,0.5,0.75,0.9];`

Compute the icdf values for the Poisson distribution at the values in p.

`x = icdf(pd,p)`
```x = 1×5 0 1 2 3 4 ```

Each value in x corresponds to a value in the input vector p. For example, at the value p equal to 0.9, the corresponding icdf value x is equal to 4.

Alternatively, you can compute the same icdf values without creating a probability distribution object. Use the `icdf` function and specify a Poisson distribution using the same value for the rate parameter $\lambda$.

`x2 = icdf('Poisson',p,lambda)`
```x2 = 1×5 0 1 2 3 4 ```

The icdf values are the same as those computed using the probability distribution object.

Create a standard normal distribution object.

`pd = makedist('Normal')`
```pd = NormalDistribution Normal distribution mu = 0 sigma = 1 ```

Determine the critical values at the 5% significance level for a test statistic with a standard normal distribution, by computing the upper and lower 2.5% values.

`x = icdf(pd,[.025,.975])`
```x = 1×2 -1.9600 1.9600 ```

Plot the cdf and shade the critical regions.

`p = normspec(x,0,1,'outside')`

```p = 0.0500 ```

## Input Arguments

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Probability distribution name, specified as one of the probability distribution names in this table.

`name`DistributionInput Parameter `A`Input Parameter `B`Input Parameter `C`Input Parameter `D`
`'Beta'`Beta Distributiona first shape parameterb second shape parameterN/AN/A
`'Binomial'`Binomial Distributionn number of trialsp probability of success for each trialN/AN/A
`'BirnbaumSaunders'`Birnbaum-Saunders Distributionβ scale parameterγ shape parameterN/AN/A
`'Burr'`Burr Type XII Distributionα scale parameterc first shape parameterk second shape parameterN/A
`'Chisquare'` or `'chi2'`Chi-Square Distributionν degrees of freedomN/AN/AN/A
`'Exponential'`Exponential Distributionμ meanN/AN/AN/A
`'Extreme Value'` or `'ev'`Extreme Value Distributionμ location parameterσ scale parameterN/AN/A
`'F'`F Distributionν1 numerator degrees of freedomν2 denominator degrees of freedomN/AN/A
`'Gamma'`Gamma Distributiona shape parameterb scale parameterN/AN/A
```'Generalized Extreme Value'``` or `'gev'`Generalized Extreme Value Distributionk shape parameterσ scale parameterμ location parameterN/A
`'Generalized Pareto'` or `'gp'`Generalized Pareto Distributionk tail index (shape) parameterσ scale parameterμ threshold (location) parameterN/A
`'Geometric'`Geometric Distributionp probability parameterN/AN/AN/A
`'Half Normal'` or `'hn'`Half-Normal Distributionμ location parameterσ scale parameterN/AN/A
`'Hypergeometric'` or `'hyge'`Hypergeometric Distributionm size of the populationk number of items with the desired characteristic in the populationn number of samples drawnN/A
`'InverseGaussian'`Inverse Gaussian Distributionμ scale parameterλ shape parameterN/AN/A
`'Logistic'`Logistic Distributionμ meanσ scale parameterN/AN/A
`'LogLogistic'`Loglogistic Distributionμ mean of logarithmic valuesσ scale parameter of logarithmic valuesN/AN/A
`'LogNormal'`Lognormal Distributionμ mean of logarithmic valuesσ standard deviation of logarithmic valuesN/AN/A
`'Loguniform'`Loguniform Distributiona lower endpoint (minimum)b upper endpoint (maximum)N/AN/A
`'Nakagami'`Nakagami Distributionμ shape parameterω scale parameterN/AN/A
`'Negative Binomial'` or `'nbin'`Negative Binomial Distributionr number of successesp probability of success in a single trialN/AN/A
`'Noncentral F'` or `'ncf'`Noncentral F Distributionν1 numerator degrees of freedomν2 denominator degrees of freedomδ noncentrality parameterN/A
`'Noncentral t'` or `'nct'`Noncentral t Distributionν degrees of freedomδ noncentrality parameterN/AN/A
`'Noncentral Chi-square'` or `'ncx2'`Noncentral Chi-Square Distributionν degrees of freedomδ noncentrality parameterN/AN/A
`'Normal'`Normal Distributionμ mean σ standard deviationN/AN/A
`'Poisson'`Poisson Distributionλ meanN/AN/AN/A
`'Rayleigh'`Rayleigh Distributionb scale parameterN/AN/AN/A
`'Rician'`Rician Distributions noncentrality parameterσ scale parameterN/AN/A
`'Stable'`Stable Distributionα first shape parameterβ second shape parameterγ scale parameterδ location parameter
`'T'`Student's t Distributionν degrees of freedomN/AN/AN/A
`'tLocationScale'`t Location-Scale Distributionμ location parameterσ scale parameterν shape parameterN/A
`'Uniform'`Uniform Distribution (Continuous)a lower endpoint (minimum)b upper endpoint (maximum)N/AN/A
`'Discrete Uniform'` or `'unid'`Uniform Distribution (Discrete)n maximum observable valueN/AN/AN/A
`'Weibull'` or `'wbl'`Weibull Distributiona scale parameterb shape parameterN/AN/A

Example: `'Normal'`

Probability values at which to evaluate the icdf, specified as a scalar value, or an array of scalar values in the range [0,1].

If one or more of the input arguments `p`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `icdf` expands each scalar input into a constant array of the same size as the array inputs. See `name` for the definitions of `A`, `B`, `C`, and `D` for each distribution.

Example: `[0.1,0.25,0.5,0.75,0.9]`

Data Types: `single` | `double`

First probability distribution parameter, specified as a scalar value or an array of scalar values.

If one or more of the input arguments `p`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `icdf` expands each scalar input into a constant array of the same size as the array inputs. See `name` for the definitions of `A`, `B`, `C`, and `D` for each distribution.

Data Types: `single` | `double`

Second probability distribution parameter, specified as a scalar value or an array of scalar values.

If one or more of the input arguments `p`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `icdf` expands each scalar input into a constant array of the same size as the array inputs. See `name` for the definitions of `A`, `B`, `C`, and `D` for each distribution.

Data Types: `single` | `double`

Third probability distribution parameter, specified as a scalar value or an array of scalar values.

If one or more of the input arguments `p`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `icdf` expands each scalar input into a constant array of the same size as the array inputs. See `name` for the definitions of `A`, `B`, `C`, and `D` for each distribution.

Data Types: `single` | `double`

Fourth probability distribution parameter, specified as a scalar value or an array of scalar values.

If one or more of the input arguments `p`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `icdf` expands each scalar input into a constant array of the same size as the array inputs. See `name` for the definitions of `A`, `B`, `C`, and `D` for each distribution.

Data Types: `single` | `double`

Probability distribution, specified as one of the probability distribution objects in this table.

Distribution ObjectFunction or App to Create Probability Distribution Object
`BetaDistribution``makedist`, `fitdist`, Distribution Fitter
`BinomialDistribution``makedist`, `fitdist`, Distribution Fitter
`BirnbaumSaundersDistribution``makedist`, `fitdist`, Distribution Fitter
`BurrDistribution``makedist`, `fitdist`, Distribution Fitter
`ExponentialDistribution``makedist`, `fitdist`, Distribution Fitter
`ExtremeValueDistribution``makedist`, `fitdist`, Distribution Fitter
`GammaDistribution``makedist`, `fitdist`, Distribution Fitter
`GeneralizedExtremeValueDistribution``makedist`, `fitdist`, Distribution Fitter
`GeneralizedParetoDistribution``makedist`, `fitdist`, Distribution Fitter
`HalfNormalDistribution``makedist`, `fitdist`, Distribution Fitter
`InverseGaussianDistribution``makedist`, `fitdist`, Distribution Fitter
`KernelDistribution``fitdist`, Distribution Fitter
`LogisticDistribution``makedist`, `fitdist`, Distribution Fitter
`LoglogisticDistribution``makedist`, `fitdist`, Distribution Fitter
`LognormalDistribution``makedist`, `fitdist`, Distribution Fitter
`LoguniformDistribution``makedist`
`MultinomialDistribution``makedist`
`NakagamiDistribution``makedist`, `fitdist`, Distribution Fitter
`NegativeBinomialDistribution``makedist`, `fitdist`, Distribution Fitter
`NormalDistribution``makedist`, `fitdist`, Distribution Fitter
Piecewise distribution with generalized Pareto distributions in the tails`paretotails`
`PiecewiseLinearDistribution``makedist`
`PoissonDistribution``makedist`, `fitdist`, Distribution Fitter
`RayleighDistribution``makedist`, `fitdist`, Distribution Fitter
`RicianDistribution``makedist`, `fitdist`, Distribution Fitter
`StableDistribution``makedist`, `fitdist`, Distribution Fitter
`tLocationScaleDistribution``makedist`, `fitdist`, Distribution Fitter
`TriangularDistribution``makedist`
`UniformDistribution``makedist`
`WeibullDistribution``makedist`, `fitdist`, Distribution Fitter

## Output Arguments

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icdf values, returned as a scalar value or an array of scalar values. `x` is the same size as `p` after any necessary scalar expansion. Each element in `x` is the icdf value of the distribution, specified by the corresponding elements in the distribution parameters (`A`, `B`, `C`, and `D`) or specified by the probability distribution object (`pd`), evaluated at the corresponding element in `p`.

## Alternative Functionality

`icdf` is a generic function that accepts either a distribution by its name `name` or a probability distribution object `pd`. It is faster to use a distribution-specific function, such as `norminv` for the normal distribution and `binoinv` for the binomial distribution. For a list of distribution-specific functions, see Supported Distributions.

## Version History

Introduced before R2006a