@stdlib/stats-base-dists-arcsine-pdf
v0.3.1TypeScript
Arcsine distribution probability density function (PDF).
0/weekUpdated 4 days agoApache-2.0Unpacked: 60.2 KB
Published by The Stdlib Authors
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About stdlib...
Probability Density Function
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> [Arcsine][arcsine-distribution] distribution [probability density function][pdf] (PDF).
The [probability density function][pdf] (PDF) for an [arcsine][arcsine-distribution] random variable is
where a is the minimum support and b is the maximum support of the distribution. The parameters must satisfy a < b.
Installation
``bash`
npm install @stdlib/stats-base-dists-arcsine-pdf
Usage
`javascript`
var pdf = require( '@stdlib/stats-base-dists-arcsine-pdf' );
#### pdf( x, a, b )
Evaluates the [probability density function][pdf] (PDF) for an [arcsine][arcsine-distribution] distribution with parameters a (minimum support) and b (maximum support).
`javascript
var y = pdf( 2.0, 0.0, 4.0 );
// returns ~0.159
y = pdf( 5.0, 0.0, 4.0 );
// returns 0.0
y = pdf( 0.25, 0.0, 1.0 );
// returns ~0.735
`
If provided NaN as any argument, the function returns NaN.
`javascript
var y = pdf( NaN, 0.0, 1.0 );
// returns NaN
y = pdf( 0.0, NaN, 1.0 );
// returns NaN
y = pdf( 0.0, 0.0, NaN );
// returns NaN
`
If provided a >= b, the function returns NaN.
`javascript
var y = pdf( 2.5, 3.0, 2.0 );
// returns NaN
y = pdf( 2.5, 3.0, 3.0 );
// returns NaN
`
#### pdf.factory( a, b )
Returns a function for evaluating the [PDF][pdf] of an [arcsine][arcsine-distribution] distribution with parameters a (minimum support) and b (maximum support).
`javascript
var myPDF = pdf.factory( 6.0, 7.0 );
var y = myPDF( 7.0 );
// returns Infinity
y = myPDF( 5.0 );
// returns 0.0
`
Examples
`javascript
var uniform = require( '@stdlib/random-array-uniform' );
var logEachMap = require( '@stdlib/console-log-each-map' );
var pdf = require( '@stdlib/stats-base-dists-arcsine-pdf' );
var opts = {
'dtype': 'float64'
};
var x = uniform( 25, -10.0, 10.0, opts );
var a = uniform( x.length, -20.0, 0.0, opts );
var b = uniform( x.length, 0.0, 40.0, opts );
logEachMap( 'x: %0.4f, a: %0.4f, b: %0.4f, f(x;a,b): %0.4f', x, a, b, pdf );
`
*
C APIs
$3
`c`
#include "stdlib/stats/base/dists/arcsine/pdf.h"
#### stdlib_base_dists_arcsine_pdf( x, a, b )
Evaluates the probability density function (PDF) for an arcsine distribution.
`c`
double out = stdlib_base_dists_arcsine_pdf( 0.5, 0.0, 4.0 );
// returns ~0.159
The function accepts the following arguments:
- x: [in] double input value.[in] double
- a: minimum support.[in] double
- b: maximum support.
`c`
double stdlib_base_dists_arcsine_pdf( const double x, const double a, const double b );
$3
`c
#include "stdlib/stats/base/dists/arcsine/pdf.h"
#include
#include
static double random_uniform( const double min, const double max ) {
double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
return min + ( v*(max-min) );
}
int main( void ) {
double x;
double a;
double b;
double y;
int i;
for ( i = 0; i < 25; i++ ) {
x = random_uniform( -10.0, 10.0 );
a = random_uniform( -20.0, 0.0 );
b = random_uniform( a, a+40.0 );
y = stdlib_base_dists_arcsine_pdf( x, a, b );
printf( "x: %lf, a: %lf, b: %lf, f(x;a,b): %lf\n", x, a, b, y );
}
}
`
*
Notice
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
#### Community
[![Chat][chat-image]][chat-url]
---
License
See [LICENSE][stdlib-license].
Copyright
Copyright © 2016-2026. The Stdlib [Authors][stdlib-authors].
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[pdf]: https://en.wikipedia.org/wiki/Probability_density_function
[arcsine-distribution]: https://en.wikipedia.org/wiki/Arcsine_distribution