Package Exports
- @stdlib/stats-base-dists-invgamma-logpdf
- @stdlib/stats-base-dists-invgamma-logpdf/lib/index.js
This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@stdlib/stats-base-dists-invgamma-logpdf) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.
Readme
Logarithm of Probability Density Function
Evaluate the natural logarithm of the probability density function (PDF) for an inverse gamma distribution.
The probability density function (PDF) for an inverse gamma random variable is
where alpha > 0 is the shape parameter and beta > 0 is the scale parameter.
Installation
npm install @stdlib/stats-base-dists-invgamma-logpdfUsage
var logpdf = require( '@stdlib/stats-base-dists-invgamma-logpdf' );logpdf( x, alpha, beta )
Evaluates the natural logarithm of the probability density function (PDF) for an inverse gamma distribution with parameters alpha (shape parameter) and beta (rate parameter).
var y = logpdf( 2.0, 0.5, 1.0 );
// returns ~-2.112
y = logpdf( 0.2, 1.0, 1.0 );
// returns ~-1.781
y = logpdf( -1.0, 4.0, 2.0 );
// returns -InfinityIf 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 NaNIf provided alpha <= 0, the function returns NaN.
var y = logpdf( 2.0, 0.0, 1.0 );
// returns NaN
y = logpdf( 2.0, -0.5, 1.0 );
// returns NaNIf provided beta <= 0, the function returns NaN.
var y = logpdf( 2.0, 1.0, 0.0 );
// returns NaN
y = logpdf( 2.0, 1.0, -1.0 );
// returns NaNlogpdf.factory( alpha, beta )
Returns a function for evaluating the natural logarithm of the PDF for an inverse gamma distribution with parameters alpha (shape parameter) and beta (rate parameter).
var mylogPDF = logpdf.factory( 6.0, 7.0 );
var y = mylogPDF( 2.0 );
// returns ~-1.464Examples
var randu = require( '@stdlib/random-base-randu' );
var logpdf = require( '@stdlib/stats-base-dists-invgamma-logpdf' );
var alpha;
var beta;
var x;
var y;
var i;
for ( i = 0; i < 10; i++ ) {
x = randu() * 2.0;
alpha = randu() * 5.0;
beta = randu() * 5.0;
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 ) );
}Notice
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