摘要翻译：
大多数现实世界的动态系统是由不同的部件组成的，这些部件通常以非常不同的速率演化。在传统的时态图形模型中，如动态贝叶斯网络，时间是以固定的粒度建模的，通常是根据最快组件的演化速度来选择的。然后，推断必须以这种最快的粒度执行，这可能会花费大量的计算成本。连续时间贝叶斯网络（CTBNs）通过将系统建模为随时间连续演化，避免了表示中的时间切片。Nodelman等人的期望-传播(EP)推理算法。（2005）可以随着时间的推移而改变推理粒度，但粒度在系统的所有部分都是统一的，并且必须事先选择。在本文中，我们提出了一种新的EP算法，它利用了一个一般的簇图结构，其中簇包含在空间（变量集）和时间上都可以重叠的分布。这种体系结构允许系统的不同部分根据它们当前的进化速度，以非常不同的时间粒度建模。我们还提供了一个信息论准则，用于在推理过程中动态地重新划分簇，以调整逼近水平以适应当前的进化速度。这避免了手工选择适当粒度的需要，并允许在信息通过网络传输时调整粒度。我们给出的实验证明了这种方法可以节省大量的计算量。
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英文标题：
《Reasoning at the Right Time Granularity》
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作者：
Suchi Saria, Uri Nodelman, Daphne Koller
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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：
  Most real-world dynamic systems are composed of different components that often evolve at very different rates. In traditional temporal graphical models, such as dynamic Bayesian networks, time is modeled at a fixed granularity, generally selected based on the rate at which the fastest component evolves. Inference must then be performed at this fastest granularity, potentially at significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid time-slicing in the representation by modeling the system as evolving continuously over time. The expectation-propagation (EP) inference algorithm of Nodelman et al. (2005) can then vary the inference granularity over time, but the granularity is uniform across all parts of the system, and must be selected in advance. In this paper, we provide a new EP algorithm that utilizes a general cluster graph architecture where clusters contain distributions that can overlap in both space (set of variables) and time. This architecture allows different parts of the system to be modeled at very different time granularities, according to their current rate of evolution. We also provide an information-theoretic criterion for dynamically re-partitioning the clusters during inference to tune the level of approximation to the current rate of evolution. This avoids the need to hand-select the appropriate granularity, and allows the granularity to adapt as information is transmitted across the network. We present experiments demonstrating that this approach can result in significant computational savings.
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PDF链接：
https://arxiv.org/pdf/1206.5260