Dynamic Bayesian Networks: A State of the Art
01 Oct 2001-CTIT technical report series (University of Twente, Centre for Telematics and Information Technology)-
About: 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).
••27 Oct 2008
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.
Abstract: Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics have generally focused on measuring individual vulnerabilities without considering their combined effects. Our previous work tackle this issue by exploring the causal relationships between vulnerabilities encoded in an attack graph. However, the evolving nature of vulnerabilities and networks has largely been ignored. In this paper, we propose a Dynamic Bayesian Networks (DBNs)-based model to incorporate temporal factors, such as the availability of exploit codes or patches. Starting from the model, we study two concrete cases to demonstrate the potential applications. This novel model provides a theoretical foundation and a practical framework for continuously measuring network security in a dynamic environment.
••01 Sep 2006
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.
Abstract: 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. A static fatigue model that captures the static relationships between fatigue, significant factors that cause fatigue, and various sensory observations that typically result from fatigue is first presented. Such a model provides mathematically coherent and sound basis for systematically aggregating uncertain evidences from different sources, augmented with relevant contextual information. The static model, however, fails to capture the dynamic aspect of fatigue. Fatigue is a cognitive state that is developed over time. To account for the temporal aspect of human fatigue, the static fatigue model is extended based on dynamic Bayesian networks. The dynamic fatigue model allows to integrate fatigue evidences not only spatially but also temporally, therefore, leading to a more robust and accurate fatigue modeling and inference. A real-time nonintrusive fatigue monitor was built based on integrating the proposed fatigue model with a computer vision system developed for extracting various visual cues typically related to fatigue. Performance evaluation of the fatigue monitor using both synthetic and real data demonstrates the validity of the proposed fatigue model in both modeling and real-time inference of fatigue
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.
Abstract: Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
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.
Abstract: Video semantic analysis is formulated based on the low-level image features and the high-level knowledge which is encoded in abstract, nongeometric representations. This paper introduces a semantic analysis system based on Bayesian network (BN) and dynamic Bayesian network (DBN). It is validated in the particular domain of soccer game videos. Based on BN/DBN, it can identify the special events in soccer games such as goal event, corner kick event, penalty kick event, and card event. The video analyzer extracts the low-level evidences, whereas the semantic analyzer uses BN/DBN to interpret the high-level semantics. Different from previous shot-based semantic analysis approaches, the proposed semantic analysis is frame-based for each input frame, it provides the current semantics of the event nodes as well as the hidden nodes. Another contribution is that the BN and DBN are automatically generated by the training process instead of determined by ad hoc. The last contribution is that we introduce a so-called temporal intervening network to improve the accuracy of the semantics output
••07 Apr 2008
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.
Abstract: In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, 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. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.
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