Dynamic Bayesian network modeling for longitudinal brain morphometry
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Dynamic Bayesian network
Brain morphometry
Longitudinal data
The paper proposes a general hierarchical Bayesian methodology for the analysis of binary longitudinal data. The Bayesian method is implemented via Markov Chain Monte Carlo (MCMC) integration technique and is applied to a real dataset which was collected to study stereotyped behavior in deer mice.
Longitudinal data
Binary data
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A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. In this paper we present a decision support system based on a dynamic Bayesian network. Its purpose is to monitor the dry weight of patients suffering from chronic renal failure treated by hemodialysis.
Dynamic Bayesian network
Graphical model
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본 논문은 스마트 홈에서 베이지안 네트워크에 기반을 둔 보편성을 가지는 상황인식 시스템의 구현방법을 제안한다. 베이지안 네트워크는 각 센서정보를 바탕으로 거주자의 활동 및 스마트 홈의 상황에 대한 추론을 확률적으로 접근하는데 매우 유용한 수단이다. 하지만 센서 정보와 활동정보가 다양해짐에 따라 기존의 방법으로는 베이지만 네트워크를 구성하기가 힘들다. 따라서 본 논문에서는 상호정보를 통하여 보다 효율적으로 베이지안 네트워크를 구성하도록 하며, 시뮬레이션을 통하여 자료 취득하고 그에 따른 거주자의 활동인식의 결과를 보인다. This paper deals with a context-aware application based on Bayesian network in the smart home. Bayesian network is a powerful graphical tool for learning casual dependencies between various context events and obtaining probability distributions. So we can recognize the resident's activities and home environment based on it. However as the sensors become various, learning the structure become difficult. We construct Bayesian network simple and efficient way with mutual information and evaluated the method in the virtual smart home.
Dynamic Bayesian network
Graphical model
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Based on the hierarchical prior method,we study the Bayesian local influence of the mixed-effects model for longitudinal data.Two types of perturbation schemes are proposed based on the characteristics of longitudinal data which include both individuals and individual cases.The Bayesian local influential method and formulas of parameter estimates are provided under these perturbation schemes.An example is given for illustration.
Longitudinal data
Mixed model
Longitudinal Study
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Accidents often occur in the waters of inland river bridge areas, which have a great impact on bridge safety, life safety and environmental pollution. This paper build a Bayesian network (BN) to connect the various types of accidents, from may affect the safety of navigation environment, the person's problem, ships, four aspects of the natural environment were analyzed, and the division of ship's security level and early warning indicators, through the analysis of the opinions of the experts in this field, optimization of Bayesian networks is conditional probability tables. Using Genie to construct the Bayesian network structure diagram, the Bayesian network can be re-inferred by changing the state of nodes. The research results show that the model has a more accurate evaluation result for the navigation risk of ships in the bridge area, and can be used to remind the ships and drivers of the navigation risk in the bridge area in different situations.
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The method based on the Bayesian networks to assess situation is one of the main methods in the domain of situation assessment.While,traditional Bayesian networks have no temporal semantic,so they can't solve the problem of temporal reasoning for situatio assessment.So,we rebuild the Bayesian networks and research on the methods of constructing temporal Bayesian networks.Furthermore,show the process nand method to construct a temporal Bayesian network and to reason using the constructed network by a scenario of the battlefield,prove the validity of this method.
Dynamic Bayesian network
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High speed train control systems are complex, realtime, and distributed systems. Failure of any of such subsystems can have heavy impact on the service itself, leading to obvious deterioration of performance, reduction of perceived quality and increment of costs. This paper proposed a Bayesian network based fault diagnosis and maintenance for high-speed trains control systems. Firstly, a Bayesian network based fault model was generated by Bayesian learning from fault table. Then, the maximum possible fault cause through the reverse reasoning ability of the Bayesian network was deduced. Finally, a Dynamic Bayesian Network (DBN) based maintenance model was presented and the real-time maintenance results of high-speed train control systems was used to verify the efficiency of the proposed algorithm.
Dynamic Bayesian network
Table (database)
High speed train
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The aim of this communication is to present a new way of how to structure modelling process of complex and large scale systems by object oriented Bayesian network (OOBN) for risk assessment and management purpose. In the first stage, we extend OOBN by presenting a new definition that introduces some flexibility, in a second stage, dynamic Bayesian networks (DBN) described by OOBN method are presented, that leads to a framework that we refer to as Dynamic Objet Oriented Bayesian Network (DOOBN). A demonstration in the domain of risk assessment of flash floods effect on the infrastructures inoperability is considered to show potential applicability of the extended OOBN.
Dynamic Bayesian network
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