论文标题
CTBN的结构学习通过惩罚的最大似然方法
Structure learning for CTBN's via penalized maximum likelihood methods
论文作者
论文摘要
连续时间的贝叶斯网络（CTBN）代表一类随机过程，可用于模拟复杂现象，例如，它们可以描述生活过程中，社会科学模型或医学中发生的相互作用。有关该主题的文献通常集中在系统的依赖结构已知并确定条件过渡强度（网络的参数）时。在本文中，我们研究了结构学习问题，这是一项更具挑战性的任务，现有的有关该主题的研究是有限的。我们建议的方法是基于惩罚的可能性方法。我们证明我们的算法在轻度的规律性条件下，识别图形的依赖性结构很高。我们还研究了该程序在数值研究中的特性，以证明其有效性。
The continuous-time Bayesian networks (CTBNs) represent a class of stochastic processes, which can be used to model complex phenomena, for instance, they can describe interactions occurring in living processes, in social science models or in medicine. The literature on this topic is usually focused on the case when the dependence structure of a system is known and we are to determine conditional transition intensities (parameters of the network). In the paper, we study the structure learning problem, which is a more challenging task and the existing research on this topic is limited. The approach, which we propose, is based on a penalized likelihood method. We prove that our algorithm, under mild regularity conditions, recognizes the dependence structure of the graph with high probability. We also investigate the properties of the procedure in numerical studies to demonstrate its effectiveness.
