基于频繁事件挖掘的动态贝叶斯网络推理
Inferring Dynamic Bayesian Networks using Frequent Episode Mining
摘要：
动机：已经提出了几种不同的研究思路来建模和挖掘时态数据。一方面，动态贝叶斯网络（DBN）等方法为时间索引随机变量之间的关系建模提供了正式的概率基础，但这些模型在一般情况下难以学习。另一方面，频繁情节挖掘等算法可扩展到大型数据集，但没有表现出严格的概率解释，这是图形模型文献的支柱。结果如下：我们提出了一个统一的这两个看似不同的线程的研究，通过演示如何动态（离散）贝叶斯网络可以推断频繁事件挖掘的结果。这有助于将前者的建模重点与后者的计数重点联系起来。首先，我们展示了如何在合理的假设下，数据的特性和随机变量的影响，最佳DBN结构可以使用贪婪的，本地的算法计算。接下来，我们将DBN结构的最优性与固定延迟事件及其不同事件的计数的概念联系起来。最后，为了证明我们的方法的实际可行性，我们专注于一个特定的（但广泛适用的）类网络，称为兴奋性网络，并显示如何搜索最佳DBN结构可以进行使用频繁发作的信息。应用数据集收集的尖峰神经元的数学模型，以及真正的神经科学数据集。可用性：可从我们的网站http://neural-code.cs.vt.edu/dbn获得模拟器实现、模拟器代码库和数据集
英文摘要：
Motivation: Several different threads of research have been proposed for modeling and mining temporal data. On the one hand, approaches such as dynamic Bayesian networks (DBNs) provide a formal probabilistic basis to model relationships between time-indexed random variables but these models are intractable to learn in the general case. On the other, algorithms such as frequent episode mining are scalable to large datasets but do not exhibit the rigorous probabilistic interpretations that are the mainstay of the graphical models literature. Results: We present a unification of these two seemingly diverse threads of research, by demonstrating how dynamic (discrete) Bayesian networks can be inferred from the results of frequent episode mining. This helps bridge the modeling emphasis of the former with the counting emphasis of the latter. First, we show how, under reasonable assumptions on data characteristics and on influences of random variables, the optimal DBN structure can be computed using a greedy, local, algorithm. Next, we connect the optimality of the DBN structure with the notion of fixed-delay episodes and their counts of distinct occurrences. Finally, to demonstrate the practical feasibility of our approach, we focus on a specific (but broadly applicable) class of networks, called excitatory networks, and show how the search for the optimal DBN structure can be conducted using just information from frequent episodes. Application on datasets gathered from mathematical models of spiking neurons as well as real neuroscience datasets are presented. Availability: Algorithmic implementations, simulator codebases, and datasets are available from our website at http://neural-code.cs.vt.edu/dbn
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
