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Artículos de revistas sobre el tema "Bayesian Networks"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Puga, Jorge López, Martin Krzywinski, and Naomi Altman. "Bayesian networks." Nature Methods 12, no. 9 (2015): 799–800. http://dx.doi.org/10.1038/nmeth.3550.

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2

Darwiche, Adnan. "Bayesian networks." Communications of the ACM 53, no. 12 (2010): 80–90. http://dx.doi.org/10.1145/1859204.1859227.

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3

Heckerman, David, and Michael P. Wellman. "Bayesian networks." Communications of the ACM 38, no. 3 (1995): 27–30. http://dx.doi.org/10.1145/203330.203336.

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4

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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5

Burnside, Elizabeth S. "Bayesian networks." Academic Radiology 12, no. 4 (2005): 422–30. http://dx.doi.org/10.1016/j.acra.2004.11.030.

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6

Jensen, Finn V. "Bayesian networks." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 3 (2009): 307–15. http://dx.doi.org/10.1002/wics.48.

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7

Beerenwinkel, Niko, Nicholas Eriksson, and Bernd Sturmfels. "Conjunctive Bayesian networks." Bernoulli 13, no. 4 (2007): 893–909. http://dx.doi.org/10.3150/07-bej6133.

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8

Atienza, David, Concha Bielza, and Pedro Larrañaga. "Semiparametric Bayesian networks." Information Sciences 584 (January 2022): 564–82. http://dx.doi.org/10.1016/j.ins.2021.10.074.

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9

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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10

Delucchi, Matteo, Jonas I. Liechti, Georg R. Spinner, and Reinhard Furrer. "Additive Bayesian Networks." Journal of Open Source Software 9, no. 101 (2024): 6822. http://dx.doi.org/10.21105/joss.06822.

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11

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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12

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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13

Kononenko, I. "Bayesian neural networks." Biological Cybernetics 61, no. 5 (1989): 361–70. http://dx.doi.org/10.1007/bf00200801.

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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

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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16

Flores, Miguel, Diego Heredia, Roberto Andrade, and Mariam Ibrahim. "Smart Home IoT Network Risk Assessment Using Bayesian Networks." Entropy 24, no. 5 (2022): 668. http://dx.doi.org/10.3390/e24050668.

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A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur in the IoT network. The parameters of the Bayesian network are estimated with the maximum likelihood method applied to a data set obtained from the simulation of attacks, in five simulation scenarios. For the risk assessment, inferences in the Bayesian network and the impact of the attacks are considered, focusing on DoS attacks, M

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17

Gao, Xiaoguang, Yu Yang, and Zhigao Gao. "Learning Bayesian networks by constrained Bayesian estimation." Journal of Systems Engineering and Electronics 30, no. 03 (2019): 511–24. http://dx.doi.org/10.21629/jsee.2019.03.09.

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18

Gopnik, Alison, and Joshua B. Tenenbaum. "Bayesian networks, Bayesian learning and cognitive development." Developmental Science 10, no. 3 (2007): 281–87. http://dx.doi.org/10.1111/j.1467-7687.2007.00584.x.

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19

Moreira, Catarina, and Andreas Wichert. "Are quantum-like Bayesian networks more powerful than classical Bayesian networks?" Journal of Mathematical Psychology 82 (February 2018): 73–83. http://dx.doi.org/10.1016/j.jmp.2017.11.003.

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20

Alonzo, Ann Lorraine D. C., Jealarr Joseph M. Campos, Luis Lloyd M. Layco, Charmaine A. Maratas, and Ria A. Sagum. "ENTDEx: ENT Diagnosis Expert System Using Bayesian Networks." Journal of Advances in Computer Networks 2, no. 3 (2014): 182–87. http://dx.doi.org/10.7763/jacn.2014.v2.108.

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21

Ramírez, Carlos, and Guillermo De la Torre-Gea. "Perception of Security in Mexico through Bayesian Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1244–52. http://dx.doi.org/10.31142/ijtsrd10702.

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22

MacKay, David J. C. "Bayesian neural networks and density networks." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 354, no. 1 (1995): 73–80. http://dx.doi.org/10.1016/0168-9002(94)00931-7.

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23

DEL ÁGUILA, ISABEL MARÍA, and JOSÉ DEL SAGRADO. "REQUIREMENT RISK LEVEL FORECAST USING BAYESIAN NETWORKS CLASSIFIERS." International Journal of Software Engineering and Knowledge Engineering 21, no. 02 (2011): 167–90. http://dx.doi.org/10.1142/s0218194011005219.

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Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification perform

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24

Litvinenko, Alexander, Natalya Litvinenko, Orken Mamyrbayev, and Assem Shayakhmetova. "GENERATIONS IN BAYESIAN NETWORKS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9, no. 3 (2019): 10–13. http://dx.doi.org/10.35784/iapgos.228.

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This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents belonging to previous generations. Terminology for this concept, in our opinion, has not yet fully developed. The concept of “Generation” in some cases makes it easier to solve some problems in Bayesian networks and to build simpler algorithms.  In this paper we will consider the well-known example “Asia”, described in many articles and books, as

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25

Sanghai, S., P. Domingos, and D. Weld. "Relational Dynamic Bayesian Networks." Journal of Artificial Intelligence Research 24 (December 2, 2005): 759–97. http://dx.doi.org/10.1613/jair.1625.

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Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to fir

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26

Vagis, Alexandra A. "Learning on Bayesian Networks." Journal of Automation and Information Sciences 34, no. 6 (2002): 5. http://dx.doi.org/10.1615/jautomatinfscien.v34.i6.40.

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27

Chen, Cong, and Changhe Yuan. "Learning Diverse Bayesian Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7793–800. http://dx.doi.org/10.1609/aaai.v33i01.33017793.

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Much effort has been directed at developing algorithms for learning optimal Bayesian network structures from data. When given limited or noisy data, however, the optimal Bayesian network often fails to capture the true underlying network structure. One can potentially address the problem by finding multiple most likely Bayesian networks (K-Best) in the hope that one of them recovers the true model. However, it is often the case that some of the best models come from the same peak(s) and are very similar to each other; so they tend to fail together. Moreover, many of these models are not even o

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28

Ghosh, Malay, Tapabrata Maiti, Dalho Kim, Sounak Chakraborty, and Ashutosh Tewari. "Hierarchical Bayesian Neural Networks." Journal of the American Statistical Association 99, no. 467 (2004): 601–8. http://dx.doi.org/10.1198/016214504000000665.

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29

Needham, Chris J., James R. Bradford, Andrew J. Bulpitt, and David R. Westhead. "Inference in Bayesian networks." Nature Biotechnology 24, no. 1 (2006): 51–53. http://dx.doi.org/10.1038/nbt0106-51.

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30

McNaught, Ken, and Andy Chan. "Bayesian networks in manufacturing." Journal of Manufacturing Technology Management 22, no. 6 (2011): 734–47. http://dx.doi.org/10.1108/17410381111149611.

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31

Bauer, Alexander, and Claudia Czado. "Pair-Copula Bayesian Networks." Journal of Computational and Graphical Statistics 25, no. 4 (2016): 1248–71. http://dx.doi.org/10.1080/10618600.2015.1086355.

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32

Langseth, Helge, and Luigi Portinale. "Bayesian networks in reliability." Reliability Engineering & System Safety 92, no. 1 (2007): 92–108. http://dx.doi.org/10.1016/j.ress.2005.11.037.

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33

Wang, ShuangCheng, GuangLin Xu, and RuiJie Du. "Restricted Bayesian classification networks." Science China Information Sciences 56, no. 7 (2013): 1–15. http://dx.doi.org/10.1007/s11432-012-4729-x.

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34

Liu, Manxia, Arjen Hommersom, Maarten van der Heijden, and Peter J. F. Lucas. "Hybrid time Bayesian networks." International Journal of Approximate Reasoning 80 (January 2017): 460–74. http://dx.doi.org/10.1016/j.ijar.2016.02.009.

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35

Di Zio, Marco, Mauro Scanu, Lucia Coppola, Orietta Luzi, and Alessandra Ponti. "Bayesian networks for imputation." Journal of the Royal Statistical Society: Series A (Statistics in Society) 167, no. 2 (2004): 309–22. http://dx.doi.org/10.1046/j.1467-985x.2003.00736.x.

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36

Hong, Junping, and Ercan Engin Kuruoglu. "Minimax Bayesian Neural Networks." Entropy 27, no. 4 (2025): 340. https://doi.org/10.3390/e27040340.

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Robustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field. Recently, researchers have applied the closed-loop idea to neural networks via the minimax method and proposed the closed-loop neural networks. In this paper, we study more conservative BNNs with the minimax method, which formulates a two-player game between a deterministic neural network and a sampling stochastic neural network. From this perspective, we reveal the connection between the

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37

Angelopoulos, Nicos, and James Cussens. "Bayesian learning of Bayesian networks with informative priors." Annals of Mathematics and Artificial Intelligence 54, no. 1-3 (2008): 53–98. http://dx.doi.org/10.1007/s10472-009-9133-x.

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38

Liu, Xiang Ke, Zhi Shen Wang, Hai Liang Wang, and Jun Tao Wang. "Application of Bayesian Network in Safety Evaluation of Metro Construction." Advanced Materials Research 838-841 (November 2013): 1463–68. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1463.

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The paper introduced the Bayesian networks briefly and discussed the algorithm of transforming fault tree into Bayesian networks at first, then regarded the structures impaired caused by tunnel blasting construction as a example, introduced the built and calculated method of the Bayesian networks by matlab. Then assumed the probabilities of essential events, calculated the probability of top event and the posterior probability of each essential events by the Bayesian networks. After that the paper contrast the characteristics of fault tree analysis and the Bayesian networks, Identified that th

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39

Milns, Isobel, Colin M. Beale, and V. Anne Smith. "Revealing ecological networks using Bayesian network inference algorithms." Ecology 91, no. 7 (2010): 1892–99. http://dx.doi.org/10.1890/09-0731.1.

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40

Wang, Changda, and Elisa Bertino. "Sensor Network Provenance Compression Using Dynamic Bayesian Networks." ACM Transactions on Sensor Networks 13, no. 1 (2017): 1–32. http://dx.doi.org/10.1145/2997653.

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41

Pe'er, D. "Bayesian Network Analysis of Signaling Networks: A Primer." Science Signaling 2005, no. 281 (2005): pl4. http://dx.doi.org/10.1126/stke.2812005pl4.

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42

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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43

Mørup, Morten, and Mikkel N. Schmidt. "Bayesian Community Detection." Neural Computation 24, no. 9 (2012): 2434–56. http://dx.doi.org/10.1162/neco_a_00314.

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Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communiti

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44

Kłopotek, Mieczysław Alojzy. "A New Bayesian Tree Learning Method with Reduced Time and Space Complexity." Fundamenta Informaticae 49, no. 4 (2002): 349–67. https://doi.org/10.3233/fun-2002-49405.

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Bayesian networks have many practical applications due to their capability to represent joint probability distribution in many variables in a compact way. There exist efficient reasoning methods for Bayesian networks. Many algorithms for learning Bayesian networks from empirical data have been developed. A well-known problem with Bayesian networks is the practical limitation for the number of variables for which a Bayesian network can be learned in reasonable time. A remarkable exception here is the Chow/Liu algorithm for learning tree-like Bayesian networks. However, its quadratic time and sp

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45

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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46

Pinacho-Ríos, Araceli, and Guillermo De la Torre-Gea. "Analysis of Maternal Deaths in Oaxaca through Bayesian Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1658–63. http://dx.doi.org/10.31142/ijtsrd10758.

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47

Pinacho, Maria del Carmen Santos, and Guillermo De la Torre-Gea. "Availability and Internet Access in Homes through Bayesian Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1646–51. http://dx.doi.org/10.31142/ijtsrd10759.

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48

Wagner, Philipp, Xinyang Wu, and Marco F. Huber. "Kalman Bayesian Neural Networks for Closed-Form Online Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10069–77. http://dx.doi.org/10.1609/aaai.v37i8.26200.

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Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameters, i.e., the weights. Training the weight distribution of a BNN, however, is more involved due to the intractability of the underlying Bayesian inference problem and thus, requires efficient approximations. In this paper, we propose a novel approach for BNN learning via closed-form Bayesian inference. For this purpose, the calculation of the predictive distribution of the output and the update of the weight distribut

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49

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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50

Becker, Ann-Kristin, Marcus Dörr, Stephan B. Felix, et al. "From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach." PLOS Computational Biology 17, no. 2 (2021): e1008735. http://dx.doi.org/10.1371/journal.pcbi.1008735.

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In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning

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