说明：  MCMC方法是一种重要的模拟计算方法，马尔可夫链蒙特卡尔理论(Markov chain Monte Carlo：MCMC)的研究对建立可实际应用的统计模型开辟了广阔的前景。90年代以来，很多应用问题都存在着分析对象比较复杂与正确识别模型结构的困难。现在根据MCMC理论，通过使用专用统计软件进行MCMC模拟，可解决许多复杂性问题。此外，得益于MCMC理论的运用，使得贝叶斯(Bayes)统计得到了再度复兴，以往被认为不可能实施计算的统计方法变得是很轻而易举了。
(The MCMC method is an important simulation method. The research of Markov chain Monte Carlo:MCMC (Markov) has opened up a broad prospect for the establishment of a practically applicable statistical model. Since 90s, many application problems have been difficult to analyze the complexity of the analysis objects and to identify the structure of the model correctly. Now, according to the MCMC theory, MCMC simulation can be done by using special statistical software to solve many complex problems. In addition, thanks to the application of MCMC theory, Bias's (Bayes) statistics has been revived again, and the statistical method which was previously considered impossible to carry out the calculation became very easy.)