code: public void setMeanCovariance(Vec mean, Matrix covariance)
      {
          if(!covariance.isSquare())
              throw new ArithmeticException("Covariance matrix must be square");
          else if(mean.length() != covariance.rows())
              throw new ArithmeticException("The mean vector and matrix must have the same dimension," +
                      mean.length() + " does not match [" + covariance.rows() + ", " + covariance.rows() +"]" );
          //Else, we are good!
          this.mean = mean.clone();
          setCovariance(covariance);
      }
docs: Sets the mean and covariance for this distribution. For an <i>n</i> dimensional distribution,
  <tt>mean</tt> should be of length <i>n</i> and <tt>covariance</tt> should be an <i>n</i> by <i>n</i> matrix.
  It is also a requirement that the matrix be symmetric positive definite.
  @param mean the mean for the distribution. A copy will be used.
  @param covariance the covariance for this distribution. A copy will be used.
  @throws ArithmeticException if the <tt>mean</tt> and <tt>covariance</tt> do not agree, or the covariance is not
  positive definite. An exception may not be throw for all bad matrices.