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Using Bayesian network on network tomography

Weiping Zhu
2003 Computer Communications  
In this paper, we propose an approach in the multicast class that uses the Bayesian network to carry out statistical inference.  ...  A number of methods have been proposed in recent years, which can be divided into two classes: multicast-based and unicast-based.  ...  More recently, researchers have developed methods for learning the uncertain knowledge from data, which is a new and fast evolving technique, and more importantly, even from incomplete data, the parameters  ... 
doi:10.1016/s0140-3664(02)00131-7 fatcat:xrvvq6zgpzgz7cljfonsp6s2ay

Bayesian networks for supporting query processing over incomplete autonomous databases

Rohit Raghunathan, Sushovan De, Subbarao Kambhampati
2013 Journal of Intelligent Information Systems  
Our approach involves mining/"learning" Bayesian networks from a sample of the database, and using it to do both imputation (predict a missing value) and query rewriting (retrieve relevant results with  ...  We learn this distribution in terms of Bayesian networks.  ...  However, learning and inference on Bayesian networks can be computationally expensive which might inhibit their applications to handling incompleteness in autonomous data sources.  ... 
doi:10.1007/s10844-013-0277-0 fatcat:ghxs4a4t6zgzfpcgt2kglizbou

Propagating imprecise probabilities in Bayesian networks

Gernot D. Kleiter
1996 Artificial Intelligence  
It is shown that the propagation of Dirichlet distributions in Bayesian networks with incomplete data results in a system of probability mixtures of beta-binomial and Dirichlet distributions.  ...  Approximate first order probabilities and their second order probability density functions are obtained by stochastic simulation.  ...  The 6 method Gibbs sampling allows the propagation of first order probabilities in Bayesian networks with incomplete data.  ... 
doi:10.1016/s0004-3702(96)00021-5 fatcat:nyegvccb4jgcjdxvo6hrtrcbtm

Bayesian Network for Uncertainty Representation in Semantic Web: A Survey

Kumar Ravi, Sheopujan Singh
2013 International Journal of Computer Applications Technology and Research  
using Bayesian inference.  ...  This paper aims at giving an overall view, the work carried out so far to represent uncertainty with the help of Bayesian Network in semantic web and a list of works done using MEBN/PR-OWL for knowledge  ...  INTRODUCTION Bayesian network is widely used method for the representation of uncertain data and knowledge [1] .  ... 
doi:10.7753/ijcatr0205.1006 fatcat:x3gqylqk3va6zkykrtryqe2gaa

Dynamic Knowledge Inference Based on Bayesian Network Learning

Deyan Wang, Adam AmrilJaharadak, Ying Xiao, Chenxi Huang
2020 Mathematical Problems in Engineering  
Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters.  ...  After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results.  ...  Uncertain knowledge representation can be divided into two categories. e first is a probabilitybased method, including a Bayesian network, dynamic causal network, and Markov network. e second one is a  ... 
doi:10.1155/2020/6613896 fatcat:zzviwtdag5cmzkxdd2pubwpzym

Bayes Networks for Supporting Query Processing Over Incomplete Autonomous Databases [article]

Rohit Raghunathan, Sushovan De, Subbarao Kambhampati
2012 arXiv   pre-print
Our approach involves mining/"learning" Bayes networks from a sample of the database, and using it to do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness  ...  We learn this distribution in terms of Bayes networks.  ...  The markov blanket of a node in a Bayesian network consists of its parent nodes, children nodes and children's other parent nodes.  ... 
arXiv:1208.5745v1 fatcat:o6ih7zdjnre7pnoxbm2uei7dv4

An Overview of Bayesian Network Applications in Uncertain Domains

Khalid Iqbal, Xu-Cheng Yin, Hong-Wei Hao, Qazi Mudassar Ilyas, Hazrat Ali
2015 Journal of clean energy technologies  
The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains.  ...  BN techniques can be used in these domains for prediction and decision support.  ...  Bayesian Networks Learning Once the nodes and their states to be modeled have been identified, a BN can be constructed in two ways.  ... 
doi:10.7763/ijcte.2015.v7.996 fatcat:qlhgx3kuevenxlvn3q5uu5lubu

The threshold EM algorithm for parameter learning in bayesian network with incomplete data [article]

Fradj Ben Lamine, Karim Kalti, Mohamed Ali Mahjoub
2012 arXiv   pre-print
Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning.  ...  In real application, training data are always incomplete or some nodes are hidden.  ...  THE THRESHOLD EM ALGORITHM FOR PARAMETER LEARNING IN BAYESIAN NETWORK WITH INCOMPLETE DATA The set of parameter in bayesian network using EM algorithm is approximate.  ... 
arXiv:1204.1681v1 fatcat:7kqyiwf7tfel7jc4zbue42fmhy

Chapter 11 Bayesian Networks [chapter]

A. Darwiche
2008 Foundations of Artificial Intelligence  
A Bayesian network is a tool for modeling and reasoning with uncertain beliefs.  ...  Intuitively, the DAG of a Bayesian network explicates variables of interest (DAG nodes) and the direct influences among them (DAG edges).  ...  Mark Chavira, Arthur Choi, Rina Dechter, and David Poole provided valuable comments on different versions of this chapter.  ... 
doi:10.1016/s1574-6526(07)03011-8 fatcat:yypbezypcbeazo6ehprmqrrdci

A Tutorial on Learning With Bayesian Networks [article]

David Heckerman
2022 arXiv   pre-print
With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data.  ...  Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention.  ...  For example, if we construct a Bayesian network for the fraud problem using the ordering (J, G, S, A, F ), we obtain a fully connected network structure.  ... 
arXiv:2002.00269v3 fatcat:q27rdi35nvafjgqeawkewc3nm4

A Multi-Agent System with Distributed Bayesian Reasoning for Network Fault Diagnosis [chapter]

Álvaro Carrera, Javier Gonzalez-Ordás, Javier García-Algarra, Pablo Arozarena, Mercedes Garijo
2011 Advances in Intelligent and Soft Computing  
In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented.  ...  An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems.  ...  Acknowledgement This research has been funded by the Spanish Ministry of Science and Innovation through the project and T2C2 (TIN2008-06739-C04-03/TSI).  ... 
doi:10.1007/978-3-642-19875-5_15 dblp:conf/paams/Carrera-BarrosoGAAG11 fatcat:w3ioipe4wrdorl4mmghirvodkm

A Mobile Picture Tagging System Using Tree-Structured Layered Bayesian Networks

Young-Seol Lee, Sung-Bae Cho
2013 Mobile Information Systems  
In order to overcome the constraints of the mobile environment, the method uses two layered Bayesian networks.  ...  To evaluate the performance of this method, an experiment is conducted with data collected over a month. The result shows the efficiency and effectiveness of our proposed method.  ...  Activity inference using tree structured Bayesian network The model has to satisfy two conditions to infer a user's activity on a mobile phone.  ... 
doi:10.1155/2013/794726 fatcat:ljxjlttnjzak7gziaxk4bobnzy

The threshold EM algorithm for parameter learning in bayesian network with incomplete data

Fradj Ben, Karim Kalti, Mohamed Ali
2011 International Journal of Advanced Computer Science and Applications  
Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning.  ...  In real application, training data are always incomplete or some nodes are hidden.  ...  THE THRESHOLD EM ALGORITHM FOR PARAMETER LEARNING IN BAYESIAN NETWORK WITH INCOMPLETE DATA The set of parameter in bayesian network using EM algorithm is approximate.  ... 
doi:10.14569/ijacsa.2011.020713 fatcat:jwta4k2pbjdndnwgxtfmodg3zi

Bayesian Network Approach to Customer Requirements to Customized Product Model

Qin Yang, Zhirui Li, Haisen Jiao, Zufang Zhang, Weijie Chang, Daozhu Wei
2019 Discrete Dynamics in Nature and Society  
First, the company built a custom product model based on Bayesian networks. According to the model, the customer selects some nodes and their related information.  ...  Finally, an example of a multifunctional nursing bed is used to illustrate the effectiveness of the method.  ...  Acknowledgments This research is supported by the National Natural Science Foundation of China (no. 51775156), and the China Scholarship Council (no. 201706695019).  ... 
doi:10.1155/2019/9687236 fatcat:2cykl2oehjexzd2n5uyacdmrhm

Learning Bayesian networks: approaches and issues

Rónán Daly, Qiang Shen, Stuart Aitken
2011 Knowledge engineering review (Print)  
Bayesian networks have become a widely used method in the modelling of uncertain knowledge.  ...  This work takes a broad look at the literature on learning Bayesian networks-in particular their structure-from data.  ...  Stuart Aitken is funded by BBSRC grant BB/F015976/1, and by the Centre for Systems Biology at Edinburgh, a Centre for Integrative Systems Biology (CISB) funded by BBSRC and EPSRC, reference BB/ D019621  ... 
doi:10.1017/s0269888910000251 fatcat:schmhrymdjewrggsdq7vmx23uu
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