该模型采用ARMA模型描述实测流量的先验分布，采用AR模型模拟预报残差的似然函数，并假定先验分布和似然函数均服从正态分布。
The ARMA model was used to describe the prior distribution of observed discharge and the ar model was adopted to simulate the likelihood function of forecasting error.
贝叶斯推断中边际似然函数涉及到维数较高的复杂积分的计算，因而精确地计算边际似然函数往往有困难。
In Bayesian reference, marginal likelihood function involve to compute high dimensional complex integrand. So exactly to compute marginal likelihood is often difficult.
这里我们得到了原始似然函数的下界，EM算法的核心就是通过最大化这个下界来最大化原始似然函数。
The main idea of EM is to maximize this lower bound so as to maximize the original (incompelte) likelihood.
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