Hybrid Time Bayesian Networks

被引：1

作者：

Liu, Manxia ^{[1
]}

Hommersom, Arjen ^{[1
,2
]}

van der Heijden, Maarten ^{[1
]}

Lucas, Peter J. F. ^{[1
]}

机构：

[1] Radboud Univ Nijmegen, ICIS, NL-6525 ED Nijmegen, Netherlands

[2] Open Univ, Heerlen, Netherlands

来源：

SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2015 | 2015年 / 9161卷

关键词：

Continuous time Bayesian networks; Dynamic Bayesian networks; Dynamic systems;

D O I：

10.1007/978-3-319-20807-7_34

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Capturing heterogeneous dynamic systems in a probabilistic model is a challenging problem. A single time granularity, such as employed by dynamic Bayesian networks, provides insufficient flexibility to capture the dynamics of many real-world processes. The alternative is to assume that time is continuous, giving rise to continuous time Bayesian networks. Here the problem is that the level of temporal detail is too precise to match available probabilistic knowledge. In this paper, we present a novel class of models, called hybrid time Bayesian networks, which combine discrete-time and continuous-time Bayesian networks. The new formalism allows us to more naturally model dynamic systems with regular and irregularly changing variables. Its usefulness is illustrated by means of a real-world medical problem.

引用

页码：376 / 386

页数：11