time-to-botec/js/node_modules/@stdlib/stats/base/dists/negative-binomial/logpmf

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.

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 ) );
}