Holzlöhner Ronald, Menyuk Curtis R
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, TBC 201-B, 1000 Hilltop Circle, Baltimore, Maryland 21250, USA.
Opt Lett. 2003 Oct 15;28(20):1894-6. doi: 10.1364/ol.28.001894.
We apply the multicanonical Monte Carlo (MMC) method to compute the probability distribution of the received voltage in a chirped return-to-zero system. When computing the probabilities of very rare events, the MMC technique greatly enhances the efficiency of Monte Carlo simulations by biasing the noise realizations. Our results agree with the covariance matrix method over 20 orders of magnitude. The MMC method can be regarded as iterative importance sampling that automatically converges toward the optimal bias so that it requires less a priori knowledge of the simulated system than importance sampling requires. A second advantage is that the merging of different regions of a probability distribution function to obtain the entire function is not necessary in many cases.
我们应用多正则蒙特卡罗（MMC）方法来计算啁啾归零系统中接收电压的概率分布。在计算极罕见事件的概率时，MMC技术通过对噪声实现进行偏置，极大地提高了蒙特卡罗模拟的效率。我们的结果在超过20个数量级上与协方差矩阵方法一致。MMC方法可被视为迭代重要性采样，它会自动朝着最优偏置收敛，因此与重要性采样相比，它对模拟系统的先验知识要求更少。第二个优点是，在许多情况下，无需合并概率分布函数的不同区域来获得整个函数。
