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摘要翻译：
在本报告中，我们将感兴趣的动态贝叶斯网络(DBNs)作为一个模型，试图结合时间维和不确定性。我们从DBN的基础开始，在这里我们特别关注推理和学习概念和算法。然后，我们将介绍创建DBN的不同层次和方法，以及在静态贝叶斯网络中引入时间维的方法。
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英文标题：
《Characterization of Dynamic Bayesian Network》
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作者：
Nabil ghanmy, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara
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最新提交年份：
2012
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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。
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英文摘要：
In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms. Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian network.
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PDF链接：
https://arxiv.org/pdf/1204.1637
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