Logarithm of Probability Density Function

Beta distribution logarithm of probability density function (PDF).

The probability density function (PDF) for a beta random variable is

where alpha > 0 is the first shape parameter and beta > 0 is the second shape parameter.

Usage

var logpdf = require( '@stdlib/stats/base/dists/beta/logpdf' );

logpdf( x, alpha, beta )

Evaluates the natural logarithm of the probability density function (PDF) for a beta distribution with parameters alpha (first shape parameter) and beta (second shape parameter).

var y = logpdf( 0.5, 0.5, 1.0 );
// returns ~-0.347

y = logpdf( 0.1, 1.0, 1.0 );
// returns 0.0

y = logpdf( 0.8, 4.0, 2.0 );
// returns ~0.717

If provided an input value x outside the support [0,1], the function returns -Infinity.

var y = logpdf( -0.1, 1.0, 1.0 );
// returns -Infinity

y = logpdf( 1.1, 1.0, 1.0 );
// returns -Infinity

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

var y = logpdf( NaN, 1.0, 1.0 );
// returns NaN

y = logpdf( 0.0, NaN, 1.0 );
// returns NaN

y = logpdf( 0.0, 1.0, NaN );
// returns NaN

If provided alpha <= 0, the function returns NaN.

var y = logpdf( 0.5, 0.0, 1.0 );
// returns NaN

y = logpdf( 0.5, -1.0, 1.0 );
// returns NaN

If provided beta <= 0, the function returns NaN.

var y = logpdf( 0.5, 1.0, 0.0 );
// returns NaN

y = logpdf( 0.5, 1.0, -1.0 );
// returns NaN

logpdf.factory( alpha, beta )

Returns a function for evaluating the natural logarithm of the PDF for a beta distribution with parameters alpha (first shape parameter) and beta (second shape parameter).

var mylogPDF = logpdf.factory( 0.5, 0.5 );

var y = mylogPDF( 0.8 );
// returns ~-0.228

y = mylogPDF( 0.3 );
// returns ~-0.364

Notes

In virtually all cases, using the logpdf or logcdf functions is preferable to manually computing the logarithm of the pdf or cdf, respectively, since the latter is prone to overflow and underflow.

Examples

var randu = require( '@stdlib/random/base/randu' );
var EPS = require( '@stdlib/constants/float64/eps' );
var logpdf = require( '@stdlib/stats/base/dists/beta/logpdf' );

var alpha;
var beta;
var x;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    x = randu();
    alpha = ( randu()*5.0 ) + EPS;
    beta = ( randu()*5.0 ) + EPS;
    y = logpdf( x, alpha, beta );
    console.log( 'x: %d, α: %d, β: %d, ln(f(x;α,β)): %d', x.toFixed( 4 ), alpha.toFixed( 4 ), beta.toFixed( 4 ), y.toFixed( 4 ) );
}