@stdlib/stats-base-dists-t-logpdf
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0.2.2 • Public • Published
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Logarithm of Probability Density Function

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Evaluate the natural logarithm of the probability density function (PDF) for a Student's t distribution.

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

Probability density function (PDF) for a Student's t distribution.

where v > 0 is the degrees of freedom.

Installation

npm install @stdlib/stats-base-dists-t-logpdf

Usage

var logpdf = require( '@stdlib/stats-base-dists-t-logpdf' );

logpdf( x, v )

Evaluates the natural logarithm of the probability density function (PDF) for a Student's t distribution with degrees of freedom v.

var y = logpdf( 0.3, 4.0 );
// returns ~-1.036

y = logpdf( 2.0, 0.7 );
// returns ~-2.841

y = logpdf( -1.0, 0.5 );
// returns ~-2.134

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

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

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

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

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

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

logpdf.factory( v )

Returns a function for evaluating the natural logarithm of the PDF of a Student's t distribution with degrees of freedom v.

var mylogpdf = logpdf.factory( 1.0 );
var y = mylogpdf( 3.0 );
// returns ~-3.447

y = mylogpdf( 1.0 );
// returns ~-1.838

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-t-logpdf' );

var v;
var x;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    x = (randu() * 6.0) - 3.0;
    v = randu() * 10.0;
    y = logpdf( x, v );
    console.log( 'x: %d, v: %d, ln(f(x;v)): %d', x, v, y );
}

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.

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.

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