@stdlib/stats-base-dists-poisson-logpmf

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Logarithm of Probability Mass Function

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

The probability mass function (PMF) for a Poisson random variable is

where lambda > 0 is the mean parameter.

Installation

npm install @stdlib/stats-base-dists-poisson-logpmf

Usage

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

logpmf( x, lambda )

Evaluates the natural logarithm of the probability mass function (PMF) for a Poisson distribution with mean parameter lambda.

var y = logpmf( 4.0, 3.0 );
// returns ~-1.784

y = logpmf( 1.0, 3.0 );
// returns ~-1.901

y = logpmf( -1.0, 2.0 );
// returns -Infinity

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

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

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

If provided a negative mean parameter lambda, the function returns NaN.

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

y = logpmf( 4.0, -2.0 );
// returns NaN

If provided lambda = 0, the function evaluates the natural logarithm of the PMF of a degenerate distribution centered at 0.0.

var y = logpmf( 2.0, 0.0 );
// returns -Infinity

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

logpmf.factory( lambda )

Returns a function for evaluating the natural logarithm of the probability mass function (PMF) for a Poisson distribution with mean parameter lambda.

var mylogpmf = logpmf.factory( 1.0 );
var y = mylogpmf( 3.0 );
// returns ~-2.792

y = mylogpmf( 1.0 );
// returns ~-1.0

Examples

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

var lambda;
var x;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    x = round( randu() * 10.0 );
    lambda = randu() * 10.0;
    y = logpmf( x, lambda );
    console.log( 'x: %d, λ: %d, ln(P(X=x;λ)): %d', x, lambda.toFixed( 4 ), y.toFixed( 4 ) );
}

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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

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License

See LICENSE.

Copyright

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