# Documentation

### This is machine translation

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

# pdf

Probability density function

## Syntax

``y = pdf('name',x,A)``
``y = pdf('name',x,A,B)``
``y = pdf('name',x,A,B,C)``
``y = pdf('name',x,A,B,C,D)``
``y = pdf(pd,x)``

## Description

example

````y = pdf('name',x,A)` returns the probability density function (pdf) for the one-parameter distribution family specified by `'name'` and the distribution parameter `A`, evaluated at the values in `x`.```

example

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

example

````y = pdf(pd,x)` returns the pdf of the probability distribution object `pd`, evaluated at the values in `x`.```

## Examples

collapse all

Create a standard normal distribution object with the mean equal to 0 and the standard deviation equal to 1.

```mu = 0; sigma = 1; pd = makedist('Normal',mu,sigma); ```

Define the input vector x to contain the values at which to calculate the pdf.

```x = [-2 -1 0 1 2]; ```

Compute the pdf values for the standard normal distribution at the values in x.

```y = pdf(pd,x) ```
```y = 0.0540 0.2420 0.3989 0.2420 0.0540 ```

Each value in y corresponds to a value in the input vector x. For example, at the value x equal to 1, the corresponding pdf value y is equal to 0.2420.

Alternatively, you can compute the same pdf values without creating a probability distribution object. Use the `pdf` function, and specify a standard normal distribution using the same parameter values for and .

```y2 = pdf('Normal',x,mu,sigma) ```
```y2 = 0.0540 0.2420 0.3989 0.2420 0.0540 ```

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

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

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

Define the input vector x to contain the values at which to calculate the pdf.

```x = [0 1 2 3 4]; ```

Compute the pdf values for the Poisson distribution at the values in x.

```y = pdf(pd,x) ```
```y = 0.1353 0.2707 0.2707 0.1804 0.0902 ```

Each value in y corresponds to a value in the input vector x. For example, at the value x equal to 3, the corresponding pdf value in y is equal to 0.1804.

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

```y2 = pdf('Poisson',x,lambda) ```
```y2 = 0.1353 0.2707 0.2707 0.1804 0.0902 ```

The pdf 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 ```

Specify the `x` values and compute the pdf.

```x = -3:.1:3; pdf_normal = pdf(pd,x);```

Plot the pdf.

`plot(x,pdf_normal,'LineWidth',2)`

Create a Weibull probability distribution object.

`pd = makedist('Weibull','a',5,'b',2)`
```pd = WeibullDistribution Weibull distribution A = 5 B = 2 ```

Specify the `x` values and compute the pdf.

```x = 0:.1:15; y = pdf(pd,x);```

Plot the pdf.

`plot(x,y,'LineWidth',2)`

## Input Arguments

collapse all

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

`name`DistributionInput Parameter AInput Parameter BInput Parameter CInput Parameter D
`'Beta'`Beta Distributiona: first shape parameterb: second shape parameter
`'Binomial'`Binomial Distributionn: number of trialsp: probability of success for each trial
`'BirnbaumSaunders'`Birnbaum-Saunders Distributionβ: scale parameterγ: shape parameter
`'Burr'`Burr Type XII Distributionα: scale parameterc: first shape parameterk: second shape parameter
`'Chisquare'`Chi-Square Distributionν: degrees of freedom
`'Exponential'`Exponential Distributionμ: mean
`'Extreme Value'`Extreme Value Distributionμ: location parameterσ: scale parameter
`'F'`F Distributionν1: numerator degrees of freedomν2: denominator degrees of freedom
`'Gamma'`Gamma Distributiona: shape parameterb: scale parameter
`'Generalized Extreme Value'`Generalized Extreme Value Distributionk: shape parameterσ: scale parameterμ: location parameter
`'Generalized Pareto'`Generalized Pareto Distributionk: tail index (shape) parameterσ: scale parameterμ: threshold (location) parameter
`'Geometric'`Geometric Distributionp: probability parameter
`'HalfNormal'`Half-Normal Distributionμ: location parameterσ: scale parameter
`'Hypergeometric'`Hypergeometric Distributionm: size of the populationk: number of items with the desired characteristic in the populationn: number of samples drawn
`'InverseGaussian'`Inverse Gaussian Distributionμ: scale parameterλ: shape parameter
`'Logistic'`Logistic Distributionμ: meanσ: scale parameter
`'LogLogistic'`Loglogistic Distributionμ: mean of logarithmic valuesσ: scale parameter of logarithmic values
`'Lognormal'`Lognormal Distributionμ: mean of logarithmic valuesσ: standard deviation of logarithmic values
`'Nakagami'`Nakagami Distributionμ: shape parameterω: scale parameter
`'Negative Binomial'`Negative Binomial Distributionr: number of successesp: probability of success in a single trial
`'Noncentral F'`Noncentral F Distributionν1: numerator degrees of freedomν2: denominator degrees of freedomδ: noncentrality parameter
`'Noncentral t'`Noncentral t Distributionν: degrees of freedomδ: noncentrality parameter
`'Noncentral Chi-square'`Noncentral Chi-Square Distributionν: degrees of freedomδ: noncentrality parameter
`'Normal'`Normal Distributionμ: mean σ: standard deviation
`'Poisson'`Poisson Distributionλ: mean
`'Rayleigh'`Rayleigh Distributionb: scale parameter
`'Rician'`Rician Distributions: noncentrality parameterσ: scale parameter
`'Stable'`Stable Distributionα: first shape parameterβ: second shape parameterγ: scale parameterδ: location parameter
`'T'`Student's t Distributionν: degrees of freedom
`'tLocationScale'`t Location-Scale Distributionμ: location parameterσ: scale parameterν: shape parameter
`'Uniform'`Uniform Distribution (Continuous)a: lower endpoint (minimum)b: upper endpoint (maximum)
`'Discrete Uniform'`Uniform Distribution (Discrete)n: maximum observable value
`'Weibull'`Weibull Distributiona: scale parameterb: shape parameter

Example: `'Normal'`

Values at which to evaluate the pdf, specified as a scalar value, or an array of scalar values.

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

Example: `[-1,0,3,4]`

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 `x`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `pdf` expands each scalar input into a constant array of the same size as the array inputs.

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 `x`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `pdf` expands each scalar input into a constant array of the same size as the array inputs.

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 `x`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `pdf` expands each scalar input into a constant array of the same size as the array inputs.

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 `x`, `A`, `B`, `C`, and `D` are arrays, then the array sizes must be the same. In this case, `pdf` expands each scalar input into a constant array of the same size as the array inputs.

Data Types: `single` | `double`

Probability distribution, specified as a probability distribution object created with a function or app in this table.

Function or AppDescription
`makedist`Create a probability distribution object using specified parameter values.
`fitdist`Fit a probability distribution object to sample data.
Distribution FitterFit a probability distribution to sample data using the interactive Distribution Fitter app and export the fitted object to the workspace.
`paretotails`Create a piecewise distribution object that has generalized Pareto distributions in the tails.

## Output Arguments

collapse all

pdf values, returned as a scalar value or an array of scalar values. `y` is the same size as `x` after any necessary scalar expansion. Each element in `y` is the pdf 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 `x`.

## Alternative Functionality

• `pdf` 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 `normpdf` for the normal distribution and `binopdf` for the binomial distribution. For a list of distribution-specific functions, see Supported Distributions.

• Use the Probability Distribution Function app to create an interactive plot of the cumulative distribution function (cdf) or probability density function (pdf) for a probability distribution.