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Logarithm of Probability Density Function

Triangular distribution logarithm of probability density function (PDF).

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

$$f(x;a,b,c)=\begin{cases} 0 & \text{for } x < a \\ \frac{2(x-a)}{(b-a)(c-a)} & \text{for } a \le x < c \\ \frac{2}{b-a} & \text{for } x = c \\ \frac{2(b-x)}{(b-a)(b-c)} & \text{for } c < x \le b \\ 0 & \text{for } b < x \end{cases}$$

where a is the lower limit and b is the upper limit and c is the mode.

Usage

import logpdf from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-triangular-logpdf@deno/mod.js';

You can also import the following named exports from the package:

import { factory } from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-triangular-logpdf@deno/mod.js';

logpdf( x, a, b, c )

Evaluates the natural logarithm of the probability density function (PDF) for a triangular distribution with parameters a (lower limit), b (upper limit) and c (mode).

var y = logpdf( 0.5, -1.0, 1.0, 0.0 );
// returns ~-0.693

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

y = logpdf( -10.0, -20.0, 0.0, -2.0 );
// returns ~-2.89

y = logpdf( -2.0, -1.0, 1.0, 0.0 );
// returns -Infinity

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

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

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

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

y = logpdf( 2.0, 1.0, 0.0, NaN );
// returns NaN

If provided parameters not satisfying a <= c <= b, the function returns NaN.

var y = logpdf( 1.0, 1.0, 0.0, 1.5 );
// returns NaN

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

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

logpdf.factory( a, b, c )

Returns a function for evaluating the natural logarithm of the probability density function (PDF) of a triangular distribution with parameters a (lower limit), b (upper limit) and c (mode).

var mylogpdf = logpdf.factory( 0.0, 10.0, 5.0 );
var y = mylogpdf( 2.0 );
// returns ~-2.526

y = mylogpdf( 12.0 );
// 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

import randu from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@deno/mod.js';
import logpdf from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-triangular-logpdf@deno/mod.js';

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

for ( i = 0; i < 25; i++ ) {
    x = randu() * 30.0;
    a = randu() * 10.0;
    b = a + (randu() * 40.0);
    c = a + ((b-a) * randu());
    y = logpdf( x, a, b, c );
    console.log( 'x: %d, a: %d, b: %d, c: %d, ln(f(x;a,b,c)): %d', x.toFixed( 4 ), a.toFixed( 4 ), b.toFixed( 4 ), c.toFixed( 4 ), y.toFixed( 4 ) );
}

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Notice

This package is part of stdlib, a standard library 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

Copyright © 2016-2024. The Stdlib Authors.