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;

		
		sigma = stdev( vector );

		
		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
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 .