Open AccessJournal Article
Dynamic Bayesian Networks: A State of the Art
Vojkan Mihajlovic,Milan Petkovic +1 more
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This article is published in CTIT technical report series.The article was published on 2001-10-01 and is currently open access. It has received 106 citations till now. The article focuses on the topics: Dynamic Bayesian network & State (computer science).read more
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Proceedings ArticleDOI
Measuring network security using dynamic bayesian network
TL;DR: A Dynamic Bayesian Networks-based model is proposed to incorporate temporal factors, such as the availability of exploit codes or patches, for continuously measuring network security in a dynamic environment.
Journal ArticleDOI
A probabilistic framework for modeling and real-time monitoring human fatigue
Qiang Ji,P. Lan,Carl G. Looney +2 more
TL;DR: A probabilistic framework based on the Bayesian networks for modeling and real-time inferring human fatigue by integrating information from various sensory data and certain relevant contextual information is introduced, leading to a more robust and accurate fatigue modeling and inference.
Journal ArticleDOI
A review on the computational approaches for gene regulatory network construction.
TL;DR: Six inference approaches to infer gene regulatory networks from gene expression data are discussed: Boolean network, probabilistic Booleannetwork, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesiannetwork.
Journal ArticleDOI
Semantic analysis of soccer video using dynamic Bayesian network
TL;DR: A semantic analysis system based on Bayesian network (BN) and dynamic Bayesiannetwork (DBN) that can identify the special events in soccer games such as goal event, corner kick event, penaltyKick event, and card event is introduced.
Proceedings ArticleDOI
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
Bhargav Kanagal,Amol Deshpande +1 more
TL;DR: This paper addresses the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a Probabilistic database view.