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Journal articles on the topic 'Applications of Bayesian networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Motomura, Yoichi. "Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (2004): 93–99. http://dx.doi.org/10.20965/jaciii.2004.p0093.

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Bayesian networks are probabilistic models that can be used for prediction and decision-making in the presence of uncertainty. For intelligent information processing, probabilistic reasoning based on Bayesian networks can be used to cope with uncertainty in real-world domains. In order to apply this, we need appropriate models and statistical learning methods to obtain models. We start by reviewing Bayesian network models, probabilistic reasoning, statistical learning, and related researches. Then, we introduce applications for intelligent information processing using Bayesian networks.

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2

Aussem, Alex. "Bayesian networks." Neurocomputing 73, no. 4-6 (2010): 561–62. http://dx.doi.org/10.1016/j.neucom.2009.11.001.

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3

Verduijn, Marion, Niels Peek, Peter M. J. Rosseel, Evert de Jonge, and Bas A. J. M. de Mol. "Prognostic Bayesian networks." Journal of Biomedical Informatics 40, no. 6 (2007): 609–18. http://dx.doi.org/10.1016/j.jbi.2007.07.003.

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Verduijn, Marion, Peter M. J. Rosseel, Niels Peek, Evert de Jonge, and Bas A. J. M. de Mol. "Prognostic Bayesian networks." Journal of Biomedical Informatics 40, no. 6 (2007): 619–30. http://dx.doi.org/10.1016/j.jbi.2007.07.004.

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5

Heckerman, David, Abe Mamdani, and Michael P. Wellman. "Real-world applications of Bayesian networks." Communications of the ACM 38, no. 3 (1995): 24–26. http://dx.doi.org/10.1145/203330.203334.

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6

Zheng, Cui Fang, Long Jiang, Li Qing Jiang, and Zhi Jie Wu. "Application and Research of Bayesian Network in Data Mining." Advanced Materials Research 532-533 (June 2012): 738–42. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.738.

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Data mining techniques give us a feasible method to deal with great amount of data, which is generated during the software developing. Many methods have been used in data mining, Bayesian networks become a focus currently. It is a powerful tool and can be used to do uncertain inference. Bayesian networks have several advantages for data modeling. This paper mainly discusses the definition and building of Bayesian networks, research software engineer based on data mining, and builder a application model of data mining in software engineer, description detail the core arithmetic of Bayesian netw

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7

Kenett, Ron S. "Bayesian networks: Theory, applications and sensitivity issues." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630014. http://dx.doi.org/10.1142/s2425038416300147.

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This chapter is about an important tool in the data science workbench, Bayesian networks (BNs). Data science is about generating information from a given data set using applications of statistical methods. The quality of the information derived from data analysis is dependent on various dimensions, including the communication of results, the ability to translate results into actionable tasks and the capability to integrate various data sources [R. S. Kenett and G. Shmueli, On information quality, J. R. Stat. Soc. A 177(1), 3 (2014).] This paper demonstrates, with three examples, how the applic

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Canonne, Clement L., Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart. "Testing Bayesian Networks." IEEE Transactions on Information Theory 66, no. 5 (2020): 3132–70. http://dx.doi.org/10.1109/tit.2020.2971625.

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9

Bidyuk, B., and R. Dechter. "Cutset Sampling for Bayesian Networks." Journal of Artificial Intelligence Research 28 (January 28, 2007): 1–48. http://dx.doi.org/10.1613/jair.2149.

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The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of the exact cutset-conditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, mor

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Mnatsakanyan, Z. R., H. S. Burkom, J. S. Coberly, and J. S. Lombardo. "Bayesian Information Fusion Networks for Biosurveillance Applications." Journal of the American Medical Informatics Association 16, no. 6 (2009): 855–63. http://dx.doi.org/10.1197/jamia.m2647.

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11

Iqbal, Khalid, Xu-Cheng Yin, Hong-Wei Hao, Qazi Mudassar Ilyas, and Hazrat Ali. "An Overview of Bayesian Network Applications in Uncertain Domains." International Journal of Computer Theory and Engineering 7, no. 6 (2015): 416–27. http://dx.doi.org/10.7763/ijcte.2015.v7.996.

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12

VOMLEL, JIŘÍ. "BAYESIAN NETWORKS IN EDUCATIONAL TESTING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, supp01 (2004): 83–100. http://dx.doi.org/10.1142/s021848850400259x.

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In this paper we discuss applications of Bayesian networks to educational testing. Namely, we deal with the diagnosis of person's skills. We show that when modeling dependence between skills we can get better diagnosis faster. We present results of experiments with basic operations that use fractions. The experiments suggest that the test design can benefit from a Bayesian network that models relations between skills, not only in the case of an adaptive test but also when designing a fixed (non-adaptive) test.

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13

VÉRONIQUE, DELCROIX, MAALEJ MOHAMED-AMINE, and PIECHOWIAK SYLVAIN. "BAYESIAN NETWORKS VERSUS OTHER PROBABILISTIC MODELS FOR THE MULTIPLE DIAGNOSIS OF LARGE DEVICES." International Journal on Artificial Intelligence Tools 16, no. 03 (2007): 417–33. http://dx.doi.org/10.1142/s0218213007003345.

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Multiple diagnosis methods using Bayesian networks are rooted in numerous research projects about model-based diagnosis. Some of this research exploits probabilities to make a diagnosis. Many Bayesian network applications are used for medical diagnosis or for the diagnosis of technical problems in small or moderately large devices. This paper explains in detail the advantages of using Bayesian networks as graphic probabilistic models for diagnosing complex devices, and then compares such models with other probabilistic models that may or may not use Bayesian networks.

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14

Osseiran, A. Chawki. "Qualitative Bayesian networks." Information Sciences 131, no. 1-4 (2001): 87–106. http://dx.doi.org/10.1016/s0020-0255(00)00023-2.

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15

Marshall, Adele. "Geriatric applications of Bayesian networks and survival analysis." ACM SIGBIO Newsletter 18, no. 3 (1998): 4. http://dx.doi.org/10.1145/956034.956037.

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16

Meng, Xuhui, Hessam Babaee, and George Em Karniadakis. "Multi-fidelity Bayesian neural networks: Algorithms and applications." Journal of Computational Physics 438 (August 2021): 110361. http://dx.doi.org/10.1016/j.jcp.2021.110361.

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17

Bashar, Abul, Gerard Parr, Sally McClean, Bryan Scotney, and Detlef Nauck. "Application of Bayesian Networks for Autonomic Network Management." Journal of Network and Systems Management 22, no. 2 (2013): 174–207. http://dx.doi.org/10.1007/s10922-013-9289-x.

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18

Donnelly, Patrick J., and John W. Sheppard. "Classification of Musical Timbre Using Bayesian Networks." Computer Music Journal 37, no. 4 (2013): 70–86. http://dx.doi.org/10.1162/comj_a_00210.

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In this article, we explore the use of Bayesian networks for identifying the timbre of musical instruments. Peak spectral amplitude in ten frequency windows is extracted for each of 20 time windows to be used as features. Over a large data set of 24,000 audio examples covering the full musical range of 24 different common orchestral instruments, four different Bayesian network structures, including naive Bayes, are examined and compared with two support vector machines and a k-nearest neighbor classifier. Classification accuracy is examined by instrument, instrument family, and data set size.

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19

Karlsson, R., and F. Gustafsson. "Recursive Bayesian estimation: bearings-only applications." IEE Proceedings - Radar, Sonar and Navigation 152, no. 5 (2005): 305. http://dx.doi.org/10.1049/ip-rsn:20045073.

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20

IMOTO, SEIYA, TOMOYUKI HIGUCHI, TAKAO GOTO, KOUSUKE TASHIRO, SATORU KUHARA, and SATORU MIYANO. "COMBINING MICROARRAYS AND BIOLOGICAL KNOWLEDGE FOR ESTIMATING GENE NETWORKS VIA BAYESIAN NETWORKS." Journal of Bioinformatics and Computational Biology 02, no. 01 (2004): 77–98. http://dx.doi.org/10.1142/s021972000400048x.

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We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge au

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21

Oliva, G. Medina, P. Weber, C. Simon, and B. Iung. "Bayesian networks Applications on Dependability, Risk Analysis and Maintenance." IFAC Proceedings Volumes 42, no. 5 (2009): 215–20. http://dx.doi.org/10.3182/20090610-3-it-4004.00042.

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22

Hanea, Anca, Oswaldo Morales Napoles, and Dan Ababei. "Non-parametric Bayesian networks: Improving theory and reviewing applications." Reliability Engineering & System Safety 144 (December 2015): 265–84. http://dx.doi.org/10.1016/j.ress.2015.07.027.

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23

Jean-Philippe, M. "4.8.4 Four Applications of Bayesian Networks for Systems Engineers." INCOSE International Symposium 13, no. 1 (2003): 1407–22. http://dx.doi.org/10.1002/j.2334-5837.2003.tb02714.x.

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24

Zhang, Hao, Liyu Zhu, and Shensi Xu. "Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/7109235.

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Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analy

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25

Perrin, B. E., L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. d'Alche-Buc. "Gene networks inference using dynamic Bayesian networks." Bioinformatics 19, Suppl 2 (2003): ii138—ii148. http://dx.doi.org/10.1093/bioinformatics/btg1071.

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26

Smail, Linda. "Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution." International Journal of Mathematics and Mathematical Sciences 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/845398.

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Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade. This paper presents Bayesian networks and discusses the inference problem in such models. It proposes a statement of the problem and the proposed method to compute probability distributions. It also uses D-separation for simplifying the computation of probabilities in Bayesian networks. Given a Bayesian network over a family of random variables

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27

Khodakarami, Vahid, Norman Fenton, and Martin Neil. "Project Scheduling: Improved Approach to Incorporate Uncertainty Using Bayesian Networks." Project Management Journal 38, no. 2 (2007): 39–49. http://dx.doi.org/10.1177/875697280703800205.

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Project scheduling inevitably involves uncertainty. The basic inputs (i.e., time, cost, and resources for each activity) are not deterministic and are affected by various sources of uncertainty. Moreover, there is a causal relationship between these uncertainty sources and project parameters; this causality is not modeled in current state-of-the-art project planning techniques (such as simulation techniques). This paper introduces an approach, using Bayesian network modeling, that addresses both uncertainty and causality in project scheduling. Bayesian networks have been widely used in a range

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28

Borujeni, Sima E., Saideep Nannapaneni, Nam H. Nguyen, Elizabeth C. Behrman, and James E. Steck. "Quantum circuit representation of Bayesian networks." Expert Systems with Applications 176 (August 2021): 114768. http://dx.doi.org/10.1016/j.eswa.2021.114768.

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29

Anctil, F., and N. Lauzon. "Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions." Hydrology and Earth System Sciences 8, no. 5 (2004): 940–58. http://dx.doi.org/10.5194/hess-8-940-2004.

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Abstract. Since the 1990s, neural networks have been applied to many studies in hydrology and water resources. Extensive reviews on neural network modelling have identified the major issues affecting modelling performance; one of the most important is generalisation, which refers to building models that can infer the behaviour of the system under study for conditions represented not only in the data employed for training and testing but also for those conditions not present in the data sets but inherent to the system. This work compares five generalisation approaches: stop training, Bayesian r

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30

Sun, Xingping, Chang Chen, Lu Wang, Hongwei Kang, Yong Shen, and Qingyi Chen. "Hybrid Optimization Algorithm for Bayesian Network Structure Learning." Information 10, no. 10 (2019): 294. http://dx.doi.org/10.3390/info10100294.

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Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possibl

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31

Cai, Baoping, Lei Huang, and Min Xie. "Bayesian Networks in Fault Diagnosis." IEEE Transactions on Industrial Informatics 13, no. 5 (2017): 2227–40. http://dx.doi.org/10.1109/tii.2017.2695583.

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32

Husmeier, D. "Reverse engineering of genetic networks with Bayesian networks." Biochemical Society Transactions 31, no. 6 (2003): 1516–18. http://dx.doi.org/10.1042/bst0311516.

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This paper provides a brief introduction to learning Bayesian networks from gene-expression data. The method is contrasted with other approaches to the reverse engineering of biochemical networks, and the Bayesian learning paradigm is briefly described. The article demonstrates an application to a simple synthetic toy problem and evaluates the inference performance in terms of ROC (receiver operator characteristic) curves.

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33

Nyberg, J. Brian, Bruce G. Marcot, and Randy Sulyma. "Using Bayesian belief networks in adaptive management." Canadian Journal of Forest Research 36, no. 12 (2006): 3104–16. http://dx.doi.org/10.1139/x06-108.

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Bayesian belief and decision networks are modelling techniques that are well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes, from illustrating a conceptual understanding of system relations to calculating joint probabilities for decision options and predicting outcomes of management policies. We describe the nature and capabilities of BBNs, discuss their applications to the adaptive-management process, and present a case example of adaptive management of forests and

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34

Sampson, D., and X. Z. Wang. "Application of bayesian networks to troubleshooting distillation columns." Computers & Chemical Engineering 23 (June 1999): S613—S616. http://dx.doi.org/10.1016/s0098-1354(99)80151-3.

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35

Rodin, A. S., and E. Boerwinkle. "Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels)." Bioinformatics 21, no. 15 (2005): 3273–78. http://dx.doi.org/10.1093/bioinformatics/bti505.

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36

Spiegler, Ran. "Bayesian Networks and Boundedly Rational Expectations *." Quarterly Journal of Economics 131, no. 3 (2016): 1243–90. http://dx.doi.org/10.1093/qje/qjw011.

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AbstractI present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). The DM is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels. The DM attempts to fit this model to p , resulting in a subjective belief that distorts p by factorizing it according to the graph via the standard Bayesian network formula. As a result of

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37

Chen, Ye, and Divakaran Liginlal. "Bayesian Networks for Knowledge-Based Authentication." IEEE Transactions on Knowledge and Data Engineering 19, no. 5 (2007): 695–710. http://dx.doi.org/10.1109/tkde.2007.1024.

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38

Serras, Jorge L., Susana Vinga, and Alexandra M. Carvalho. "Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks." Applied Sciences 11, no. 4 (2021): 1955. http://dx.doi.org/10.3390/app11041955.

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Outliers are observations suspected of not having been generated by the underlying process of the remaining data. Many applications require a way of identifying interesting or unusual patterns in multivariate time series (MTS), now ubiquitous in many applications; however, most outlier detection methods focus solely on univariate series. We propose a complete and automatic outlier detection system covering the pre-processing of MTS data that adopts a dynamic Bayesian network (DBN) modeling algorithm. The latter encodes optimal inter and intra-time slice connectivity of transition networks capa

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39

Vogel, K., C. Riggelsen, O. Korup, and F. Scherbaum. "The application of Bayesian networks in natural hazard analyses." Natural Hazards and Earth System Sciences Discussions 1, no. 5 (2013): 5805–54. http://dx.doi.org/10.5194/nhessd-1-5805-2013.

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Abstract. In natural hazards we face several uncertainties due to our lack of knowledge and/or the intrinsic randomness of the underlying natural processes. Nevertheless, deterministic analysis approaches are still widely used in natural hazard assessments, with the pitfall of underestimating the hazard with potentially disastrous consequences. In this paper we show that the Bayesian network approach offers a flexible framework for capturing and expressing a broad range of different uncertainties as those encountered in natural hazard assessments. Although well studied in theory, the applicati

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40

Khakzad, Nima. "A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks." Modelling 2, no. 2 (2021): 240–58. http://dx.doi.org/10.3390/modelling2020013.

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High complexity and growing interdependencies of chemical and process facilities have made them increasingly vulnerable to domino effects. Domino effects, particularly fire dominoes, are spatial-temporal phenomena where not only the location of involved units, but also their temporal entailment in the accident chain matter. Spatial-temporal dependencies and uncertainties prevailing during domino effects, arising mainly from possible synergistic effects and randomness of potential events, restrict the use of conventional risk assessment techniques such as fault tree and event tree. Bayesian net

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41

Drury, Brett, Jorge Valverde-Rebaza, Maria-Fernanda Moura, and Alneu de Andrade Lopes. "A survey of the applications of Bayesian networks in agriculture." Engineering Applications of Artificial Intelligence 65 (October 2017): 29–42. http://dx.doi.org/10.1016/j.engappai.2017.07.003.

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42

Prabhakaran, Remya, R. Krishnaprasad, Manju Nanda, and J. Jayanthi. "System Safety Analysis for Critical System Applications Using Bayesian Networks." Procedia Computer Science 93 (2016): 782–90. http://dx.doi.org/10.1016/j.procs.2016.07.294.

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43

Rodgers, Mark, and Rosa Oppenheim. "Ishikawa diagrams and Bayesian belief networks for continuous improvement applications." TQM Journal 31, no. 3 (2019): 294–318. http://dx.doi.org/10.1108/tqm-11-2018-0184.

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Purpose In continuous improvement (CI) projects, cause-and-effect diagrams are used to qualitatively express the relationship between a given problem and its root causes. However, when data collection activities are limited, and advanced statistical analyses are not possible, practitioners need to understand causal relationships. The paper aims to discuss these issues. Design/methodology/approach In this research, the authors present a framework that combines cause-and-effect diagrams with Bayesian belief networks (BBNs) to estimate causal relationships in instances where formal data collectio

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44

Ropero, Rosa F., M. Julia Flores, Rafael Rumí, and Pedro A. Aguilera. "Applications of hybrid dynamic Bayesian networks to water reservoir management." Environmetrics 28, no. 1 (2016): e2432. http://dx.doi.org/10.1002/env.2432.

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45

McCann, Robert K., Bruce G. Marcot, and Rick Ellis. "Bayesian belief networks: applications in ecology and natural resource management." Canadian Journal of Forest Research 36, no. 12 (2006): 3053–62. http://dx.doi.org/10.1139/x06-238.

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In this introduction to the following series of papers on Bayesian belief networks (BBNs) we briefly summarize BBNs, review their application in ecology and natural resource management, and provide an overview of the papers in this section. We suggest that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions, and communicating with nonexperts about making natural resource management decisions. BBNs can be used effectively to represent uncertainty in understanding and variability in ecosystem response, and the

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46

NISHII, Ryuei. "Structure Learning by Bayesian Networks and Applications to Material Quality." Proceedings of Mechanical Engineering Congress, Japan 2016 (2016): jikiin02. http://dx.doi.org/10.1299/jsmemecj.2016.jikiin02.

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47

Beuzen, T., K. D. Splinter, L. A. Marshall, I. L. Turner, M. D. Harley, and M. L. Palmsten. "Bayesian Networks in coastal engineering: Distinguishing descriptive and predictive applications." Coastal Engineering 135 (May 2018): 16–30. http://dx.doi.org/10.1016/j.coastaleng.2018.01.005.

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48

Marrone, Stefano. "Using Bayesian networks for highly available cloud-based web applications." Journal of Reliable Intelligent Environments 1, no. 2-4 (2015): 87–100. http://dx.doi.org/10.1007/s40860-015-0009-z.

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49

LIU, WEI-YI, and KUN YUE. "BAYESIAN NETWORK WITH INTERVAL PROBABILITY PARAMETERS." International Journal on Artificial Intelligence Tools 20, no. 05 (2011): 911–39. http://dx.doi.org/10.1142/s0218213011000449.

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Interval data are widely used in real applications to represent the values of quantities in uncertain situations. However, the implied probabilistic causal relationships among interval-valued variables with interval data cannot be represented and inferred by general Bayesian networks with point-based probability parameters. Thus, it is desired to extend the general Bayesian network with effective mechanisms of representation, learning and inference of probabilistic causal relationships implied in interval data. In this paper, we define the interval probabilities, the bound-limited weak conditi

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50

Liao, Zhenyu A., Charupriya Sharma, James Cussens, and Peter Van Beek. "Finding All Bayesian Network Structures within a Factor of Optimal." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7892–99. http://dx.doi.org/10.1609/aaai.v33i01.33017892.

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A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-andsearch approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approach

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