贝叶斯网络是如今处理计算机系统，处理成千上万个变量和无数个观察的标准方法。
Bayesian networks are today's standard method for handling uncertainty in computer systems, processing thousands of variables and millions of observations.
结果，特殊的贝叶斯网络还可以处理因果关系和反事实关系。
Special versions of Bayesian networks, as it turned out, can manage causal and counterfactual relationships as well.
我介绍了一个系统的方法应用贝叶斯统计推论和最大熵来处理量子蒙特卡罗模拟所得到连续虚时间的数据。
We present a systematical way to use Bayesian statistical inference and the maximum entropy to deal with the data obtained by the continue imaginary-time QMC simulations.
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