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摘要翻译：
连续时间贝叶斯网络（CTBNs）描述了在连续时间内具有有限多个状态的结构化随机过程。CTBN是一组变量上的有向（可能是循环的）依赖图，每个变量表示一个有限状态连续时间马尔可夫过程，其转移模型是其父变量的函数。我们解决了从部分观测数据学习CTBN参数和结构的问题。我们展示了如何将期望最大化(EM)和结构期望最大化(SEM)应用于CTBNs。EM算法的可用性允许我们扩展CTBNs的表示，以允许更丰富的过渡持续时间分布，称为相位分布。该类是一个具有高度表达性的半参数表示，可以任意逼近任意持续时间分布。对CTBN框架的这种扩展解决了CTBN和DBN的一个主要限制--对指数/几何分布持续时间的限制。我们在一个真实的人的寿命数据集上的实验结果表明，我们的算法从部分观测数据中学习合理的模型结构和参数，并利用相位分布取得了比DBNS更好的性能。
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英文标题：
《Expectation Maximization and Complex Duration Distributions for
Continuous Time Bayesian Networks》
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作者：
Uri Nodelman, Christian R. Shelton, 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：
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of learning the parameters and structure of a CTBN from partially observed data. We show how to apply expectation maximization (EM) and structural expectation maximization (SEM) to CTBNs. The availability of the EM algorithm allows us to extend the representation of CTBNs to allow a much richer class of transition durations distributions, known as phase distributions. This class is a highly expressive semi-parametric representation, which can approximate any duration distribution arbitrarily closely. This extension to the CTBN framework addresses one of the main limitations of both CTBNs and DBNs - the restriction to exponentially / geometrically distributed duration. We present experimental results on a real data set of people's life spans, showing that our algorithm learns reasonable models - structure and parameters - from partially observed data, and, with the use of phase distributions, achieves better performance than DBNs.
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PDF链接：
https://arxiv.org/pdf/1207.1402
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