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Cumulative Distribution Function
Erlang distribution cumulative distribution function.
The cumulative distribution function for a Erlang random variable is
where k is the shape parameter and lambda is the rate parameter. The Erlang distribution is a special case of the gamma distribution, as k is constrained to the natural numbers.
Usage
var cdf = require( '@stdlib/stats/base/dists/erlang/cdf' );
cdf( x, k, lambda )
Evaluates the cumulative distribution function (CDF) for an Erlang distribution with parameters k (shape parameter) and lambda (rate parameter).
var y = cdf( 2.0, 1, 1.0 );
// returns ~0.865
y = cdf( 2.0, 3, 1.0 );
// returns ~0.323
y = cdf( -1.0, 2, 2.0 );
// returns 0.0
y = cdf( -Infinity, 4, 2.0 );
// returns 0.0
y = cdf( +Infinity, 4, 2.0 );
// returns 1.0
If provided NaN as any argument, the function returns NaN.
var y = cdf( NaN, 1, 1.0 );
// returns NaN
y = cdf( 0.0, NaN, 1.0 );
// returns NaN
y = cdf( 0.0, 1, NaN );
// returns NaN
If not provided a nonnegative integer for k, the function returns NaN.
var y = cdf( 2.0, -2, 0.5 );
// returns NaN
y = cdf( 2.0, 0.5, 0.5 );
// returns NaN
If provided k = 0, the function evaluates the CDF of a degenerate distribution centered at 0.
var y = cdf( 2.0, 0.0, 2.0 );
// returns 1.0
y = cdf( -2.0, 0.0, 2.0 );
// returns 0.0
y = cdf( 0.0, 0.0, 2.0 );
// returns 1.0
If provided lambda <= 0, the function returns NaN.
var y = cdf( 2.0, 1, 0.0 );
// returns NaN
y = cdf( 2.0, 1, -5.0 );
// returns NaN
cdf.factory( k, lambda )
Returns a function for evaluating the cumulative distribution function for an Erlang distribution with parameters k (shape parameter) and lambda (rate parameter).
var mycdf = cdf.factory( 2, 0.5 );
var y = mycdf( 6.0 );
// returns ~0.801
y = mycdf( 2.0 );
// returns ~0.264
Examples
var randu = require( '@stdlib/random/base/randu' );
var round = require( '@stdlib/math/base/special/round' );
var cdf = require( '@stdlib/stats/base/dists/erlang/cdf' );
var lambda;
var k;
var x;
var y;
var i;
for ( i = 0; i < 20; i++ ) {
x = randu() * 10.0;
k = round( randu() * 10.0 );
lambda = randu() * 5.0;
y = cdf( x, k, lambda );
console.log( 'x: %d, k: %d, λ: %d, F(x;k,λ): %d', x.toFixed( 4 ), k, lambda.toFixed( 4 ), y.toFixed( 4 ) );
}