whole_func_string: 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;
  	}
func_code_string: 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;
  	}
func_documentation_string: 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