# Logarithm of Probability Density Function

Uniform distribution logarithm of probability density function (PDF).

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

where a is the minimum support and b is the maximum support of the distribution. The parameters must satisfy a < b.

## Usage

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


#### logpdf( x, a, b )

Evaluates the logarithm of the probability density function (PDF) for a continuous uniform distribution with parameters a (minimum support) and b (maximum support).

var y = logpdf( 2.0, 0.0, 4.0 );
// returns ~-1.386

y = logpdf( 5.0, 0.0, 4.0 );
// returns -Infinity

y = logpdf( 0.25, 0.0, 1.0 );
// returns 0.0


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

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

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

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


If provided a >= b, the function returns NaN.

var y = logpdf( 2.5, 3.0, 2.0 );
// returns NaN

y = logpdf( 2.5, 3.0, 3.0 );
// returns NaN


#### logpdf.factory( a, b )

Returns a function for evaluating the logarithm of the PDF of a continuous uniform distribution with parameters a (minimum support) and b (maximum support).

var mylogPDF = logpdf.factory( 6.0, 7.0 );
var y = mylogPDF( 7.0 );
// returns 0.0

y = mylogPDF( 5.0 );
// returns -Infinity


## 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 logpdf = require( '@stdlib/stats/base/dists/uniform/logpdf' );

var a;
var b;
var x;
var y;
var i;

for ( i = 0; i < 25; i++ ) {
x = (randu() * 20.0) - 10.0;
a = (randu() * 20.0) - 20.0;
b = a + (randu() * 40.0);
y = logpdf( x, a, b );
console.log( 'x: %d, a: %d, b: %d, ln(f(x;a,b)): %d', x.toFixed( 4 ), a.toFixed( 4 ), b.toFixed( 4 ), y.toFixed( 4 ) );
}