Logarithm of Probability Mass Function

Evaluate the natural logarithm of the probability mass function (PMF) for a negative binomial distribution.

The probability mass function (PMF) for a negative binomial random variable X is

where r > 0 is the number of successes until experiment is stopped and 0 < p <= 1 is the success probability. The random variable X denotes the number of failures until the r success is reached.

Installation

npm install @stdlib/stats-base-dists-negative-binomial-logpmf

Usage

var logpmf = require( '@stdlib/stats-base-dists-negative-binomial-logpmf' );

logpmf( x, r, p )

Evaluates the natural logarithm of the probability mass function for a negative binomial distribution with number of successes until experiment is stopped r and success probability p.

var y = logpmf( 5.0, 20.0, 0.8 );
// returns ~-1.853

y = logpmf( 21.0, 20.0, 0.5 );
// returns ~-2.818

y = logpmf( 5.0, 10.0, 0.4 );
// returns ~-4.115

y = logpmf( 0.0, 10.0, 0.9 );
// returns ~-1.054

While r can be interpreted as the number of successes until the experiment is stopped, the negative binomial distribution is also defined for non-integers r. In this case, r denotes shape parameter of the gamma mixing distribution.

var y = logpmf( 21.0, 15.5, 0.5 );
// returns ~-3.292

y = logpmf( 5.0, 7.4, 0.4 );
// returns ~-2.976

If provided a r which is not a positive number, the function returns NaN.

var y = logpmf( 2.0, 0.0, 0.5 );
// returns NaN

y = logpmf( 2.0, -2.0, 0.5 );
// returns NaN

If provided NaN as any argument, the function returns NaN.

var y = logpmf( NaN, 20.0, 0.5 );
// returns NaN

y = logpmf( 0.0, NaN, 0.5 );
// returns NaN

y = logpmf( 0.0, 20.0, NaN );
// returns NaN

If provided a success probability p outside of [0,1], the function returns NaN.

var y = logpmf( 2.0, 20, -1.0 );
// returns NaN

y = logpmf( 2.0, 20, 1.5 );
// returns NaN

logpmf.factory( r, p )

Returns a function for evaluating the natural logarithm of the probability mass function (PMF) of a negative binomial distribution with number of successes until experiment is stopped r and success probability p.

var mylogpmf = logpmf.factory( 10, 0.5 );
var y = mylogpmf( 3.0 );
// returns ~-3.617

y = mylogpmf( 10.0 );
// returns ~-2.43

Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var logpmf = require( '@stdlib/stats-base-dists-negative-binomial-logpmf' );

var i;
var r;
var p;
var x;
var y;

for ( i = 0; i < 10; i++ ) {
    x = round( randu() * 30.0 );
    r = randu() * 50.0;
    p = randu();
    y = logpmf( x, r, p );
    console.log( 'x: %d, r: %d, p: %d, ln(P(X=x;r,p)): %d', x, r, 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.

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Copyright

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