Probability Mass Function

Bernoulli distribution probability mass function (PMF).

The probability mass function (PMF) for a Bernoulli random variable is defined as

where 0 <= p <= 1 is the success probability.

Usage

var pmf = require( '@stdlib/stats/base/dists/bernoulli/pmf' );

pmf( x, p )

Evaluates the probability mass function (PMF) of a Bernoulli distribution with success probability 0 <= p <= 1.

var y = pmf( 1.0, 0.3 );
// returns 0.3

y = pmf( 0.0, 0.3 );
// returns 0.7

y = pmf( -1.0, 0.5 );
// returns 0.0

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

var y = pmf( NaN, 0.0 );
// returns NaN

y = pmf( 0.0, NaN );
// returns NaN

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

var y = pmf( 0.0, -1.0 );
// returns NaN

y = pmf( 0.0, 1.5 );
// returns NaN

pmf.factory( p )

Returns a function for evaluating the probability mass function (PMF) of a Bernoulli distribution with success probability 0 <= p <= 1.

var mypmf = pmf.factory( 0.8 );
var y = mypmf( 0.0 );
// returns 0.2

y = mypmf( 0.5 );
// returns 0.0

Examples

var randu = require( '@stdlib/random/base/randu' );
var round = require( '@stdlib/math/base/special/round' );
var pmf = require( '@stdlib/stats/base/dists/bernoulli/pmf' );

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

for ( i = 0; i < 10; i++ ) {
    x = round( randu() * 2.0 );
    p = randu();
    y = pmf( x, p );
    console.log( 'x: %d, p: %d, P( X = x; p ): %d', x, p.toFixed( 4 ), y.toFixed( 4 ) );
}