Bibliografías temáticas / Bayesian Networks

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Literatura académica sobre el tema "Bayesian Networks"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Bayesian Networks"

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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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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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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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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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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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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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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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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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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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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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Tesis sobre el tema "Bayesian Networks"

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Horsch, Michael C. "Dynamic Bayesian networks." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28909.

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Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled. Without assumptions of independence from the domain, naive computations of probability are intractible. If probability theory is to be used effectively in AI applications, the independence assumptions from the domain should be represented explicitly, and used to greatest possible advantage. One such representation is a
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Bendtsen, Marcus. "Gated Bayesian Networks." Doctoral thesis, Linköpings universitet, Databas och informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136761.

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Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis. By introducing a new type of probabilist
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Thouin, Frédéric. "Bayesian inference in networks." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104476.

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Bayesian inference is a method that can be used to estimate an unknown and/or unobservable parameter based on evidence that is accumulated over time.In this thesis, we apply Bayesian inference techniques in the context of two network-based problems.First, we consider multi-target tracking in networks with superpositional sensors, i.e., sensors that generate measurements equal to the sum of individual contributions of each target.We derive a tractable form for a novel moment-based multi-target filter called the Additive Likelihood Moment (ALM) filter. We show, through simulations, that our par
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Nodelman, Uri D. "Continuous time bayesian networks /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Fagundes, Moser Silva. "Integrating BDI model and Bayesian networks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/10422.

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Individualmente, as linhas de pesquisa da Inteligência Artificial têm proposto abordagens para a resolução de inúmeros problemas complexos do mundo real. O paradigma orientado a agentes provê os agentes autônomos, capazes de perceber os seus ambientes, reagir de acordo com diferentes circunstâncias e estabelecer interações sociais com outros agentes de software ou humanos. As redes Bayesianas fornecem uma maneira de representar graficamente as distribuições de probabilidades condicionais e permitem a realização de raciocínios probabilísticos baseados em evidências. As ontologias são especifica
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Helldin, Tove. "Explanation Methods for Bayesian Networks." Thesis, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3193.

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<p> </p><p>The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Makin
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Förstner, Johannes. "Optimizing Queries in Bayesian Networks." Thesis, Linköpings universitet, Databas och informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-86716.

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This thesis explores and compares different methods of optimizing queries in Bayesian networks. Bayesian networks are graph-structured models that model probabilistic variables and their influences on each other; a query poses the question of what probabilities certain variables assume, given observed values on certain other variables. Bayesian inference (calculating these probabilities) is known to be NP-hard in general, but good algorithms exist in practice. Inference optimization traditionally concerns itself with finding and tweaking efficient algorithms, and leaves the choice of algorithm
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Suermondt, Henri Jacques. "Explanation in Bayesian belief networks." Full text available online (restricted access), 1992. http://images.lib.monash.edu.au/ts/theses/suermondt.pdf.

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Di, Tomaso Enza. "Soft computing for Bayesian networks." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409531.

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TITO, EDISON AMERICO HUARSAYA. "BAYESIAN LEARNING FOR NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14538@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação investiga as Redes Neurais Bayesianas, que é uma nova abordagem que conjuga o potencial das redes neurais artificiais com a solidez analítica da estatística Bayesiana. Tipicamente, redes neurais convencionais como backpropagation, têm bom desempenho mas apresentam problemas de convergência, na ausência de dados suficientes de treinamento, ou problemas de mínimos locais, que trazem como conseqüência longo tempo de treinamento (esforço computacional) e possibilidades de sobre-treinamento (generalização ruim). Por e
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Libros sobre el tema "Bayesian Networks"

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Neapolitan, Richard E. Learning Bayesian networks. Pearson Prentice Hall, 2004.

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Gámez, José A., Serafín Moral, and Antonio Salmerón, eds. Advances in Bayesian Networks. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39879-0.

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Nagarajan, Radhakrishnan, Marco Scutari, and Sophie Lèbre. Bayesian Networks in R. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6446-4.

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Holmes, Dawn E., and Lakhmi C. Jain, eds. Innovations in Bayesian Networks. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85066-3.

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Koski, Timo. Bayesian networks: An introduction. John Wiley & Sons, 2009.

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M, Noble John, ed. Bayesian networks: An introduction. John Wiley & Sons, 2009.

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Koski, Timo. Bayesian networks: An introduction. Wiley, 2009.

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Neal, Radford M. Bayesian learning for neural networks. University of Toronto, Dept. of Computer Science, 1995.

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Jensen, Finn V., and Thomas D. Nielsen. Bayesian Networks and Decision Graphs. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68282-2.

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Suzuki, Joe, and Maomi Ueno, eds. Advanced Methodologies for Bayesian Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28379-1.

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Capítulos de libros sobre el tema "Bayesian Networks"

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Hassani, Bertrand K. "Bayesian Networks." In Scenario Analysis in Risk Management. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25056-4_8.

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Almond, Russell G., and Juan-Diego Zapata-Rivera. "Bayesian Networks." In Handbook of Diagnostic Classification Models. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05584-4_4.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Bayesian Networks." In Machine Learning in Medicine. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6886-4_16.

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Freno, Antonino, and Edmondo Trentin. "Bayesian Networks." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20308-4_2.

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Truong, Dothang. "Bayesian Networks." In Data Science and Machine Learning for Non-Programmers. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003162872-19.

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Højsgaard, Søren, David Edwards, and Steffen Lauritzen. "Bayesian Networks." In Graphical Models with R. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2299-0_3.

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Sebastiani, Paola, Maria M. Abad, and Marco F. Ramoni. "Bayesian Networks." In Data Mining and Knowledge Discovery Handbook. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_10.

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González-Brenes, José P., John T. Behrens, Robert J. Mislevy, Roy Levy, and Kristen E. DiCerbo. "Bayesian Networks." In The Handbook of Cognition and Assessment. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118956588.ch14.

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Kruse, Rudolf, Sanaz Mostaghim, Christian Borgelt, Christian Braune, and Matthias Steinbrecher. "Bayesian Networks." In Texts in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-42227-1_23.

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Scutari, Marco, and Jean-Baptiste Denis. "Real-World Applications of Bayesian Networks." In Bayesian Networks, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429347436-8.

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Actas de conferencias sobre el tema "Bayesian Networks"

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Ayello, Francois, Narasi Sridhar, Gerry Koch, Vinod Khare, Abdul Wahab Al-Methen, and Shabbir Safri. "Internal Corrosion Threat Assessment of Pipelines Using Bayesian Networks." In CORROSION 2014. NACE International, 2014. https://doi.org/10.5006/c2014-3851.

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Abstract Many engineers are inclined to not trust models; this is particularly true in the field of corrosion. Suspicion comes from modeled results which are inconsistent with field data. The difference between modeled results and the real world has three reasons. First, no model is accurate in all situations. Second, the input data used to run the models is never exact. And third, the operator's knowledge of the system is often missing. In order to increase confidence and reduce the gap between modeled results and field data it is necessary to address all three sources of uncertainties. A sol
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Smith, Michael, Konstantinos Pesinis, Lewis Barton, and Ian Laing. "Intelligent Corrosion Prediction Using Bayesian Networks." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13372.

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Abstract Accurate knowledge of corrosion location, severity, cause and growth rate is critical to pipeline integrity, and in line inspection (ILI) is widely regarded as the most reliable and convenient method of obtaining such knowledge. Much industry effort has therefore centered on improving the metal loss detection and sizing capabilities of ILI tools. However, when ILI data are lacking or unattainable, operators must seek alternative ways to monitor the integrity of an asset. For managing internal pipeline corrosion, Internal Corrosion Direct Assessment (ICDA) is perhaps the best known alt
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Singh, Mandeep, Muntather Almusawi, Sonu Sharma, Egamberdiyev Dilmurod, Hemalatha S, and Niranjanamurthy M. "Predictive Analytics for 5G Network Performance Using Bayesian Networks." In 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS). IEEE, 2024. http://dx.doi.org/10.1109/iicccs61609.2024.10763765.

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Jain, Swati, Francois Ayello, John A. Beavers, and Narasi Sridhar. "Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks." In CORROSION 2013. NACE International, 2013. https://doi.org/10.5006/c2013-02616.

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Abstract Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it can remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stress, external soil environment, and electrolyte chemistry beneath disbonded coatings. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult. In addition, the uncertainty in data makes the prediction of SCC challenging. The complex interactions that affect SCC failure probability can be modeled using Bayesian network models. The B
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Villa, Simone, and Fabio Stella. "Learning Continuous Time Bayesian Networks in Non-stationary Domains." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/804.

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Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.
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Sardeshmukh, Avadhut, Sreedhar Reddy, BP Gautham, and Amol Joshi. "Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.00-91.

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Cozman, Fabio Gagliardi, and Denis Deratani Mauá. "The Finite Model Theory of Bayesian Networks: Descriptive Complexity." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/727.

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We adapt the theory of descriptive complexity to Bayesian networks, to quantify the expressivity of specifications based on predicates and quantifiers. We show that Bayesian network specifications that employ first-order quantification capture the complexity class PP; by allowing quantification over predicates, the resulting Bayesian network specifications capture each class in the hierarchy PP^(NP^...^NP), a result that does not seem to have equivalent in the literature.
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Shen, Gehui, Xi Chen, and Zhihong Deng. "Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/282.

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Bayesian neural networks (BNNs) have received more and more attention because they are capable of modeling epistemic uncertainty which is hard for conventional neural networks. Markov chain Monte Carlo (MCMC) methods and variational inference (VI) are two mainstream methods for Bayesian deep learning. The former is effective but its storage cost is prohibitive since it has to save many samples of neural network parameters. The latter method is more time and space efficient, however the approximate variational posterior limits its performance. In this paper, we aim to combine the advantages of
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Liu, Yu, and Hong Man. "Network vulnerability assessment using Bayesian networks." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2005. http://dx.doi.org/10.1117/12.604240.

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BHAT, PUSHPALATHA C., and HARRISON B. PROSPER. "BAYESIAN NEURAL NETWORKS." In Proceedings of PHYSTAT05. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2006. http://dx.doi.org/10.1142/9781860948985_0032.

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Informes sobre el tema "Bayesian Networks"

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Santos, Jr, and Eugene. Computing with Bayesian Multi-Networks. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273106.

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Acemoglu, Daron, Munther Dahleh, Ilan Lobel, and Asuman Ozdaglar. Bayesian Learning in Social Networks. National Bureau of Economic Research, 2008. http://dx.doi.org/10.3386/w14040.

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Schultz, Martin T., Thomas D. Borrowman, and Mitchell J. Small. Bayesian Networks for Modeling Dredging Decisions. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada552536.

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Groeneveld, Andrew B., Stephanie G. Wood, and Edgardo Ruiz. Estimating Bridge Reliability by Using Bayesian Networks. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/39601.

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As part of an inspection, bridge inspectors assign condition ratings to the main components of a bridge’s structural system and identify any defects that they observe. Condition ratings are necessarily somewhat subjective, as they are influenced by the experience of the inspectors. In the current work, procedures were developed for making inferences on the reliability of reinforced concrete girders with defects at both the cross section and the girder level. The Bayesian network (BN) tools constructed in this work use simple structural m echanics to model the capacity of girders. By using expe
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Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0305.

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Sharma, Nikhita. Hierarchical clustering based structural learning of Bayesian networks. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-824.

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Mislevy, Robert J. Virtual Representation of IID Observations in Bayesian Belief Networks. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada280552.

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Henderson, Thomas C., V. J. Mathews, and Dan Adams. Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1004755.

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Roberts, Nancy A. Using Bayesian Networks and Decision Theory to Model Physical Security. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada411379.

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McFarland, John, and Laura Painton Swiler. Validation of the thermal challenge problem using Bayesian Belief Networks. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/875636.

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