@stdlib/stats-base-dists-geometric-logcdf
v0.2.2
Published
Geometric distribution logarithm of cumulative distribution function (CDF).
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Logarithm of Cumulative Distribution Function
Geometric distribution logarithm of cumulative distribution function.
The cumulative distribution function for a geometric random variable is
where 0 <= p <= 1
is the success probability. x
denotes the number of failures before the first success.
Installation
npm install @stdlib/stats-base-dists-geometric-logcdf
Usage
var logcdf = require( '@stdlib/stats-base-dists-geometric-logcdf' );
logcdf( x, p )
Evaluates the logarithm of the cumulative distribution function for a geometric distribution with success probability p
.
var y = logcdf( 2.0, 0.5 );
// returns ~-0.134
y = logcdf( 2.0, 0.1 );
// returns ~-1.306
If provided NaN
as any argument, the function returns NaN
.
var y = logcdf( NaN, 0.5 );
// returns NaN
y = logcdf( 0.0, NaN );
// returns NaN
If provided a success probability p
outside of [0,1]
, the function returns NaN
.
var y = logcdf( 2.0, -1.0 );
// returns NaN
y = logcdf( 2.0, 1.5 );
// returns NaN
logcdf.factory( p )
Returns a function for evaluating the logarithm of the cumulative distribution function of a geometric distribution with success probability p
var mylogcdf = logcdf.factory( 0.5 );
var y = mylogcdf( 3.0 );
// returns ~-0.065
y = mylogcdf( 1.0 );
// returns ~-0.288
Notes
- In virtually all cases, using the
logpmf
orlogcdf
functions is preferable to manually computing the logarithm of thepmf
orcdf
, respectively, since the latter is prone to overflow and underflow.
Examples
var randu = require( '@stdlib/random-base-randu' );
var logcdf = require( '@stdlib/stats-base-dists-geometric-logcdf' );
var p;
var x;
var y;
var i;
for ( i = 0; i < 10; i++ ) {
x = randu() * 5.0;
p = randu();
y = logcdf( x, p );
console.log( 'x: %d, p: %d, ln(F(x;p)): %d', x.toFixed( 4 ), p.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.