@stdlib/stats-base-dists-kumaraswamy-logpdf
v0.2.2
Published
Natural logarithm of the probability density function (PDF) for a Kumaraswamy's double bounded distribution.
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Logarithm of Probability Density Function
Evaluate the natural logarithm of the probability density function for a Kumaraswamy's double bounded distribution.
The probability density function (PDF) for a Kumaraswamy's double bounded random variable is
where a > 0
is the first shape parameter and b > 0
is the second shape parameter.
Installation
npm install @stdlib/stats-base-dists-kumaraswamy-logpdf
Usage
var logpdf = require( '@stdlib/stats-base-dists-kumaraswamy-logpdf' );
logpdf( x, a, b )
Evaluates the natural logarithm of the probability density function (PDF) for a Kumaraswamy's double bounded distribution with parameters a
(first shape parameter) and b
(second shape parameter).
var y = logpdf( 0.5, 1.0, 1.0 );
// returns 0.0
y = logpdf( 0.5, 2.0, 4.0 );
// returns ~0.523
y = logpdf( 0.2, 2.0, 2.0 );
// returns ~-0.264
y = logpdf( 0.8, 4.0, 4.0 );
// returns ~0.522
y = logpdf( -0.5, 4.0, 2.0 );
// returns -Infinity
y = logpdf( -Infinity, 4.0, 2.0 );
// returns -Infinity
y = logpdf( 1.5, 4.0, 2.0 );
// returns -Infinity
y = logpdf( +Infinity, 4.0, 2.0 );
// returns -Infinity
If provided NaN
as any argument, the function returns NaN
.
var y = logpdf( NaN, 1.0, 1.0 );
// returns NaN
y = logpdf( 0.0, NaN, 1.0 );
// returns NaN
y = logpdf( 0.0, 1.0, NaN );
// returns NaN
If provided a <= 0
, the function returns NaN
.
var y = logpdf( 2.0, -1.0, 0.5 );
// returns NaN
y = logpdf( 2.0, 0.0, 0.5 );
// returns NaN
If provided b <= 0
, the function returns NaN
.
var y = logpdf( 2.0, 0.5, -1.0 );
// returns NaN
y = logpdf( 2.0, 0.5, 0.0 );
// returns NaN
logpdf.factory( a, b )
Returns a function for evaluating the natural logarithm of the probability density function (PDF) for a Kumaraswamy's double bounded distribution with parameters a
(first shape parameter) and b
(second shape parameter).
var mylogpdf = logpdf.factory( 0.5, 0.5 );
var y = mylogpdf( 0.8 );
// returns ~-0.151
y = mylogpdf( 0.3 );
// returns ~-0.388
Notes
- In virtually all cases, using the
logpdf
orlogcdf
functions is preferable to manually computing the logarithm of thepdf
orcdf
, respectively, since the latter is prone to overflow and underflow.
Examples
var randu = require( '@stdlib/random-base-randu' );
var EPS = require( '@stdlib/constants-float64-eps' );
var logpdf = require( '@stdlib/stats-base-dists-kumaraswamy-logpdf' );
var a;
var b;
var x;
var y;
var i;
for ( i = 0; i < 10; i++ ) {
x = randu();
a = ( randu()*5.0 ) + EPS;
b = ( randu()*5.0 ) + EPS;
y = logpdf( x, a, b );
console.log( 'x: %d, a: %d, b: %d, ln(f(x;a,b)): %d', x.toFixed( 4 ), a.toFixed( 4 ), b.toFixed( 4 ), y.toFixed( 4 ) );
}
Notice
This package is part of 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, see the main project repository.
Community
License
See LICENSE.
Copyright
Copyright © 2016-2024. The Stdlib Authors.