论文标题
连续时间带时钟的贝叶斯网络
Continuous-Time Bayesian Networks with Clocks
论文作者
论文摘要
结构化的随机过程在连续时间内演变出来，这是一个广泛采用的框架，以模拟自然和工程中发生的现象。但是，通常选择这样的模型来满足马尔可夫的特性以保持障碍。此类无内存模型中最受欢迎的一种是连续的贝叶斯网络（CTBN）。在这项工作中，我们将其限制限制为指数生存时间到任意分布。当前的扩展通过辅助状态来实现这一目标，这阻碍了障碍。为了避免这种情况，我们介绍了一组节点时钟，以构建一组图形耦合的半马尔可夫链。我们提供了参数和结构推理的算法，这些算法利用局部依赖性，并对合成数据进行实验，以及通过基因调节网络的基准测试工具生成的数据集。在此过程中，我们指出了与当前CTBN扩展相比的优势。
Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering. However, such models are often chosen to satisfy the Markov property to maintain tractability. One of the more popular of such memoryless models are Continuous Time Bayesian Networks (CTBNs). In this work, we lift its restriction to exponential survival times to arbitrary distributions. Current extensions achieve this via auxiliary states, which hinder tractability. To avoid that, we introduce a set of node-wise clocks to construct a collection of graph-coupled semi-Markov chains. We provide algorithms for parameter and structure inference, which make use of local dependencies and conduct experiments on synthetic data and a data-set generated through a benchmark tool for gene regulatory networks. In doing so, we point out advantages compared to current CTBN extensions.
