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贝叶斯统计起源于英国学者贝叶斯在1763年的一篇题为“机遇理论中一个问题的解”的论文,他提出了著名的贝叶斯公式~[1]。贝叶斯统计方法与经典统计方法最根本的区别在于不仅利用总体信息和样本信息进行统计推断,而且充分利用了参数的先验信息,它将每一个不确定的参数都看成一个随机变量,通过给予先验分布,结合马尔科夫链蒙特卡洛(markov chain monte carlo,MCMC)法进行Gibbs抽样,得出参数的
Bayesian statistics originated from a thesis by British scholar Bayes in 1763 entitled “Solutions to a Problem in the Theory of Opportunities,” in which he proposed the famous Bayesian formula [1]. The most fundamental difference between the Bayesian statistical method and the classical statistical method is that not only the statistical inference is made by using the general information and the sample information but also the a priori information of the parameters is taken full advantage of. Each uncertain parameter is regarded as a random variable, By giving a prior distribution, combined with Markov chain monte carlo (MCMC) Gibbs sampling method, the parameters of