摘要翻译：
将概率括号符号(PBN)应用于多随机变量系统，初步研究了静态贝叶斯网络(BN)和概率图形模型(PGM)。本文以著名的BN学生为例，说明了BN的地方独立性和推理能力。软件包Elvira用于图形化显示学生BN。我们的研究表明，PBN为处理静态BN中与联合、边际和条件概率分布有关的许多表达式提供了一致和方便的替代方案。
---
英文标题：
《Probability Bracket Notation, Multivariable Systems and Static Bayesian
  Networks》
---
作者：
Xing M. Wang
---
最新提交年份：
2012
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
--

---
英文摘要：
  Probability Bracket Notation (PBN) is applied to systems of multiple random variables for preliminary study of static Bayesian Networks (BN) and Probabilistic Graphic Models (PGM). The famous Student BN Example is explored to show the local independences and reasoning power of a BN. Software package Elvira is used to graphically display the student BN. Our investigation shows that PBN provides a consistent and convenient alternative to manipulate many expressions related to joint, marginal and conditional probability distributions in static BN.
---
PDF链接：
https://arxiv.org/pdf/1207.5293