Continuous uniform probability density function
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Syntax
y = unifpdf(x)
y = unifpdf(x,a,b)
Description
example
y = unifpdf(x) returns the probability density function (pdf) of the standard uniform distribution, evaluated at the values in x.
example
y = unifpdf(x,a,b) returns the pdf of the continuous uniform distribution on the interval [a, b], evaluated at the values in x.
Examples
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Compute Standard and Continuous Uniform pdf
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The pdf of the standard uniform distribution is constant on the interval [0,1].
Compute the pdf of 0.2, 0.4,...,1 in the standard uniform distribution.
x = 0.2:0.2:1;y = unifpdf(x)
y = 1×5 1 1 1 1 1If x is not between a and b, unifpdf returns 0.
Compute the pdf of 1 through 5 in the continuous uniform distribution on the interval [2,4].
x2 = 1:5;unifpdf(x2,2,4)
ans = 1×5 0 0.5000 0.5000 0.5000 0If the parameter a is larger than b, unifpdf returns NaN regardless of the x input.
unifpdf(5,10,1)
ans = NaN
Input Arguments
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x — Values at which to evaluate pdf
nonnegative scalar value | array of nonnegative scalar values
Values at which to evaluate the pdf, specified as a nonnegative scalar value or an array of nonnegative scalar values.
To evaluate the pdf at multiple values, specify
xusing an array.To evaluate the pdfs of multiple distributions, specify a and b using arrays.
If one or more of the input arguments x, a, and b are arrays, then the array sizes must be the same. In this case, unifpdf expands each scalar input into a constant array of the same size as the array inputs. Each element in y is the pdf value of the distribution specified by the corresponding elements in a and b, evaluated at the corresponding element in x.
Example: [3 4 7 9]
Data Types: single | double
a — Lower endpoint
scalar value | array of scalar values
Lower endpoint of the uniform distribution, specified as a scalar value or an array of scalar values.
To evaluate the pdf at multiple values, specify x using an array.
To evaluate the pdfs of multiple distributions, specify
aand b using arrays.
If one or more of the input arguments x, a, and b are arrays, then the array sizes must be the same. In this case, unifpdf expands each scalar input into a constant array of the same size as the array inputs. Each element in y is the pdf value of the distribution specified by the corresponding elements in a and b, evaluated at the corresponding element in x.
Example: [0 -1 7 9]
Data Types: single | double
b — Upper endpoint
scalar value | array of scalar values
Upper endpoint of the uniform distribution, specified as a scalar value or an array of scalar values.
To evaluate the pdf at multiple values, specify x using an array.
To evaluate the pdfs of multiple distributions, specify a and
busing arrays.
If one or more of the input arguments x, a, and b are arrays, then the array sizes must be the same. In this case, unifpdf expands each scalar input into a constant array of the same size as the array inputs. Each element in y is the pdf value of the distribution specified by the corresponding elements in a and b, evaluated at the corresponding element in x.
Example: [1 1 10 10]
Data Types: single | double
Output Arguments
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y — pdf values
scalar value | array of scalar values
pdf values evaluated at the values in x, returned as a scalar value or an array of scalar values. y is the same size as x, a, and b after any necessary scalar expansion. Each element in y is the pdf value of the distribution specified by the corresponding elements in a and b, evaluated at the corresponding element in x.
More About
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Continuous Uniform pdf
The continuous uniform distribution is a two-parameter family of curves with a constant pdf on its interval of support, [a, b]. The parameters a and b are the endpoints of the interval.
The continuous uniform pdf is
The standard uniform distribution occurs when a = 0 and b = 1.
For more information, see Uniform Distribution (Continuous).
Alternative Functionality
unifpdfis a function specific to the continuous uniform distribution. Statistics and Machine Learning Toolbox™ also offers the generic function pdf, which supports various probability distributions. To usepdf, create a UniformDistribution probability distribution object and pass the object as an input argument or specify the probability distribution name and its parameters. Note that the distribution-specific functionunifpdfis faster than the generic functionpdf.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.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced before R2006a
See Also
UniformDistribution | pdf | unifcdf | unifinv | unifstat | unifit | unifrnd
Topics
- Uniform Distribution (Continuous)
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