lib/stats/kde/kde.js
function kde_estimator_silverman( vector ) {
		var sigma, N = vector.length, A;

		// [0] Calculate the sample standard deviation:
		sigma = stdev( vector );

		// [1] Calculate the estimator:
		A = Math.pow( ( 4/(3*N) ), 0.2 );
		return A * sigma;
	}
function kde_estimator_silverman( vector ) {
		var sigma, N = vector.length, A;

		// [0] Calculate the sample standard deviation:
		sigma = stdev( vector );

		// [1] Calculate the estimator:
		A = Math.pow( ( 4/(3*N) ), 0.2 );
		return A * sigma;
	}
end FUNCTION linspace() 
FUNCTION: kde_estimator_silverman( vector )
Use's Silverman's rule of thumb to derive an empirical estimate for an optimal KDE bandwidth selection.
Source:
Silverman, B.W. (1998). Density Estimation for Statistics and Data Analysis. London: Chapman & Hall/CRC. p. 48. ISBN 0-412-24620-1.

@private
@param {array} vector - 1d array over which to compute the estimate
@returns {number} bandwidth estimate