Bibliographies thématiques / Continuous Time Bayesian Network

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Littérature scientifique sur le sujet « Continuous Time Bayesian Network »

Auteur : Grafiati
Publié le 9 mars 2023
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Continuous Time Bayesian Network"

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Stella, F., and Y. Amer. "Continuous time Bayesian network classifiers." Journal of Biomedical Informatics 45, no. 6 (2012): 1108–19. http://dx.doi.org/10.1016/j.jbi.2012.07.002.

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Codecasa, Daniele, and Fabio Stella. "Learning continuous time Bayesian network classifiers." International Journal of Approximate Reasoning 55, no. 8 (2014): 1728–46. http://dx.doi.org/10.1016/j.ijar.2014.05.005.

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Xu, J., and C. R. Shelton. "Intrusion Detection using Continuous Time Bayesian Networks." Journal of Artificial Intelligence Research 39 (December 23, 2010): 745–74. http://dx.doi.org/10.1613/jair.3050.

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Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed upda
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Bhattacharjya, Debarun, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush Varshney, and Dharmashankar Subramanian. "Event-Driven Continuous Time Bayesian Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3259–66. http://dx.doi.org/10.1609/aaai.v34i04.5725.

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We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We d
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Shelton, C. R., and G. Ciardo. "Tutorial on Structured Continuous-Time Markov Processes." Journal of Artificial Intelligence Research 51 (December 23, 2014): 725–78. http://dx.doi.org/10.1613/jair.4415.

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A continuous-time Markov process (CTMP) is a collection of variables indexed by a continuous quantity, time. It obeys the Markov property that the distribution over a future variable is independent of past variables given the state at the present time. We introduce continuous-time Markov process representations and algorithms for filtering, smoothing, expected sufficient statistics calculations, and model estimation, assuming no prior knowledge of continuous-time processes but some basic knowledge of probability and statistics. We begin by describing "flat" or unstructured Markov processes and
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Sturlaugson, Liessman, and John W. Sheppard. "Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models." SIAM/ASA Journal on Uncertainty Quantification 3, no. 1 (2015): 346–69. http://dx.doi.org/10.1137/140953848.

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Codecasa, Daniele, and Fabio Stella. "Classification and clustering with continuous time Bayesian network models." Journal of Intelligent Information Systems 45, no. 2 (2014): 187–220. http://dx.doi.org/10.1007/s10844-014-0345-0.

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Boudali, H., and J. B. Dugan. "A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework." IEEE Transactions on Reliability 55, no. 1 (2006): 86–97. http://dx.doi.org/10.1109/tr.2005.859228.

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Villa, S., and F. Stella. "A continuous time Bayesian network classifier for intraday FX prediction." Quantitative Finance 14, no. 12 (2014): 2079–92. http://dx.doi.org/10.1080/14697688.2014.906811.

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Gatti, E., D. Luciani, and F. Stella. "A continuous time Bayesian network model for cardiogenic heart failure." Flexible Services and Manufacturing Journal 24, no. 4 (2011): 496–515. http://dx.doi.org/10.1007/s10696-011-9131-2.

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Thèses sur le sujet "Continuous Time Bayesian Network"

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CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.

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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian net
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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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Fan, Yu. "Continuous time Bayesian Network approximate inference and social network applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://proquest.umi.com/pqdweb?index=0&did=1957308751&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268330625&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.
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ACERBI, ENZO. "Continuos time Bayesian networks for gene networks reconstruction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/52709.

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Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and improve the models' expressiveness. Continuous time Bayesian network
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VILLA, SIMONE. "Continuous Time Bayesian Networks for Reasoning and Decision Making in Finance." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/69953.

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L'analisi dell'enorme quantità di dati finanziari, messi a disposizione dai mercati elettronici, richiede lo sviluppo di nuovi modelli e tecniche per estrarre efficacemente la conoscenza da utilizzare in un processo decisionale informato. Lo scopo della tesi concerne l'introduzione di modelli grafici probabilistici utilizzati per il ragionamento e l'attività decisionale in tale contesto. Nella prima parte della tesi viene presentato un framework che utilizza le reti Bayesiane per effettuare l'analisi e l'ottimizzazione di portafoglio in maniera olistica. In particolare, esso sfrutta, da un l
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GATTI, ELENA. "Graphical models for continuous time inference and decision making." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19575.

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Reasoning about evolution of system in time is both an important and challenging task. We are interested in probability distributions over time of events where often observations are irregularly spaced over time. Probabilistic models have been widely used to accomplish this task but they have some limits. Indeed, Hidden Markov Models and Dynamic Bayesian Networks in general require the specification of a time granularity between consecutive observations. This requirement leads to computationally inefficient learning and inference procedures when the adopted time granularity is finer than the
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Alharbi, Randa. "Bayesian inference for continuous time Markov chains." Thesis, University of Glasgow, 2019. http://theses.gla.ac.uk/40972/.

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Continuous time Markov chains (CTMCs) are a flexible class of stochastic models that have been employed in a wide range of applications from timing of computer protocols, through analysis of reliability in engineering, to models of biochemical networks in molecular biology. These models are defined as a state system with continuous time transitions between the states. Extensive work has been historically performed to enable convenient and flexible definition, simulation, and analysis of continuous time Markov chains. This thesis considers the problem of Bayesian parameter inference on these mo
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Parton, Alison. "Bayesian inference for continuous-time step-and-turn movement models." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20124/.

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This thesis concerns the statistical modelling of animal movement paths given observed GPS locations. With observations being in discrete time, mechanistic models of movement are often formulated as such. This popularity remains despite an inability to compare analyses through scale invariance and common problems handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow, often excused by a difficulty in interpreting the ‘instantaneous’ parameters associated with a continuous-time model. The aim here was to bolster usage by
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Elshamy, Wesam Samy. "Continuous-time infinite dynamic topic models." Diss., Kansas State University, 2012. http://hdl.handle.net/2097/15176.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>William Henry Hsu<br>Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the collection that resembles the topical theme found in an article at hand. The first wave of topic models developed were able to discover the prevailing topics in a big collection of
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Acciaroli, Giada. "Calibration of continuous glucose monitoring sensors by time-varying models and Bayesian estimation." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3425746.

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Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide frequent (e.g., 1-5 min sampling rate) real-time measurements of glucose concentration for several consecutive days. This can be of great help in the daily management of diabetes. Most of the CGM systems commercially available today have a wire-based electrochemical sensor, usually placed in the subcutaneous tissue, which measures a "raw" electrical current signal via a glucose-oxidase electrochemical reaction. Observations of the raw electrical signal are frequently revealed by the sensor
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Livres sur le sujet "Continuous Time Bayesian Network"

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Das, Monidipa, and Soumya K. Ghosh. Enhanced Bayesian Network Models for Spatial Time Series Prediction. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27749-9.

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Ghosh, Soumya K., and Monidipa Das. Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics. Springer, 2020.

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Ghosh, Soumya K., and Monidipa Das. Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics. Springer, 2019.

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Butz, Martin V., and Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key t
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Ramsay, James. Curve registration. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.9.

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This article deals with curve registration, which refers to methods for aligning prominent features in a set of curves by transforming their abscissa variables. It first illustrates the concepts of amplitude and phase variation schematically and with real data before defining the time-warping functions and their functional inverse. It then describes the decomposition of total mean squared variation into separate amplitude and phase components, along with an R2 measure of the proportion of functional variation due to phase in a sample of curves. It also considers landmark registration, novel wa
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Unger, Herwig, and Wolfgang A. Halang, eds. Autonomous Systems 2016. VDI Verlag, 2016. http://dx.doi.org/10.51202/9783186848109.

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To meet the expectations raised by the terms Industrie 4.0, Industrial Internet and Internet of Things, real innovations are necessary, which can be brought about by information processing systems working autonomously. Owing to their growing complexity and their embedding in complex environments, their design becomes increasingly critical. Thus, the topics addressed in this book span from verification and validation of safety-related control software and suitable hardware designed for verifiability to be deployed in embedded systems over approaches to suppress electromagnetic interferences to
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Chapitres de livres sur le sujet "Continuous Time Bayesian Network"

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Shi, Dongyu, and Jinyuan You. "Update Rules for Parameter Estimation in Continuous Time Bayesian Network." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_17.

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Liu, Manxia, Fabio Stella, Arjen Hommersom, and Peter J. F. Lucas. "Representing Hypoexponential Distributions in Continuous Time Bayesian Networks." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91479-4_47.

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van der Heijden, Maarten, and Arjen Hommersom. "Causal Independence Models for Continuous Time Bayesian Networks." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_33.

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Cerotti, Davide, and Daniele Codetta-Raiteri. "Mean Field Analysis for Continuous Time Bayesian Networks." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91632-3_12.

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Acerbi, Enzo, and Fabio Stella. "Continuous Time Bayesian Networks for Gene Network Reconstruction: A Comparative Study on Time Course Data." In Bioinformatics Research and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08171-7_16.

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Codecasa, Daniele, and Fabio Stella. "A Classification Based Scoring Function for Continuous Time Bayesian Network Classifiers." In New Frontiers in Mining Complex Patterns. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_3.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Probabilistic Graphical Model for Continuous Variables." In Intelligent Control and Learning Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_14.

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AbstractMost of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.
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Kaeding, Matthias. "Continuous Time Models." In Bayesian Analysis of Failure Time Data Using P-Splines. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-08393-9_6.

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Fan, Chenglin, Jun Luo, and Binhai Zhu. "Continuous-Time Moving Network Voronoi Diagram." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25249-5_5.

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Yi, Zhang, and K. K. Tan. "Other Models of Continuous Time Recurrent Neural Networks." In Network Theory and Applications. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-3819-3_7.

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Actes de conférences sur le sujet "Continuous Time Bayesian Network"

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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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wei, chenghao, Yingying Liu, Chen Li, Song Chen, and Pukai Wang. "New constraint-based Bayesian network structure learning method for continuous variables." In International Conference on Optics, Electronics, and Communication Engineering, edited by Yang Yue. SPIE, 2024. http://dx.doi.org/10.1117/12.3048449.

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Dissanayake, Sahan, Ragil Krishna, Pubudu N. Pathirana, Malcolm K. Horne, David J. Smulewicz, and Louise A. Corben. "Continuous Optimization of a Hierarchical Bayesian Network for Friedreich’s Ataxia Severity Classification." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781628.

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Ben Abdallah, Walid, Hasna Njah, and Salma Jamoussi. "New Dynamic Bayesian Network for Time-to-Event Prediction." In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA). IEEE, 2024. https://doi.org/10.1109/aiccsa63423.2024.10912526.

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Zhu, Fenghao, Xinquan Wang, Chongwen Huang, et al. "Robust Continuous-Time Beam Tracking with Liquid Neural Network." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10900942.

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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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Größl, Martin. "Modeling dependable systems with continuous time Bayesian networks." In SAC 2015: Symposium on Applied Computing. ACM, 2015. http://dx.doi.org/10.1145/2695664.2695729.

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Perreault, Logan, Monica Thornton, Shane Strasser, and John W. Sheppard. "Deriving prognostic continuous time Bayesian networks from D-matrices." In 2015 IEEE AUTOTESTCON. IEEE, 2015. http://dx.doi.org/10.1109/autest.2015.7356482.

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Poropudas, Jirka, and Kai Virtanen. "Simulation metamodeling in continuous time using dynamic Bayesian networks." In 2010 Winter Simulation Conference - (WSC 2010). IEEE, 2010. http://dx.doi.org/10.1109/wsc.2010.5679098.

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Schupbach, Jordan, Elliott Pryor, Kyle Webster, and John Sheppard. "Combining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modeling." In 2022 IEEE AUTOTESTCON. IEEE, 2022. http://dx.doi.org/10.1109/autotestcon47462.2022.9984758.

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Rapports d'organisations sur le sujet "Continuous Time Bayesian Network"

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Shanahan, Erin, Rob Daley, Lydia Druin, Kristin Legg, and Sonya Daw. Status of whitebark pine in the Greater Yellowstone Ecosystem: A step-trend analysis with comparisons from 2004 to 2023. National Park Service, 2025. https://doi.org/10.36967/2313989.

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Whitebark pine (Pinus albicaulis) is a high-elevation conifer of the northern Rocky Mountains and the Pacific Northwest. This slow-growing, long-lived conifer influences critical ecosystem functions in subalpine environments, including snow capture, landscape stability, soil amelioration, and overall forest health and resilience. Moreover, its nutritious seeds feed the federally threatened grizzly bear (Ursus arctos), Clark’s nutcracker (Nucifraga columbiana), red squirrel (Tamiasciurus hudsonicus), and other species. However, whitebark pine is declining in high-elevation forests due to severa
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Zhao, Binghao, Yu Wang, and Wenbin Ma. Comparative Efficacy and Safety of Therapeutics for Elderly Glioblastoma: a Bayesian Network Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.3.0094.

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Review question / Objective: At this time, a comprehensive systematic review and network meta-analysis (NMA) was conducted to: (1) fill the research gap by giving rankings on treatment efficacy; (2) provide statistical evidence of not head-to-head comparisons; (3) seek out the best and up-to-date therapeutic strategy reported in latest RCTs; (4) address potential adverse events (AEs) of available treatments. Condition being studied: The incidence of glioblastoma (GBM) increases with age, until now, there has been less evidence on the optimal treatments for elderly GBM since only general GBM po
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He, zhe, liwei Xing, ming He, yuhuan Sun, jinlong Xu, and rong Zhao. Effect of Acupuncture on Mammary Gland Hyperplasia (MGH): a Bayesian network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0058.

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Review question / Objective: This review aims at conducting a network meta-analysis to assess the potential therapeutic effectiveness and safety of acupuncture therapy for the treatment of MGH. Condition being studied: MGH is a benign breast disease caused by excessive growth of mammary duct epithelial cells and interstitial fibers. Its prevalence rate among women of childbearing age is about 13.5-42%, accounting for 99.3% of the total number of patients with breast related diseases, and its possibility of developing breast cancer can reach 5-10%. Breast hyperplasia can cause clinical symptoms
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Hardy, Chris, Muthu Gandi, Adam Burry, and Desmond Power. PR-271-143716-R02 Bayesian Belief Network (BBN) Decision Support for Pipeline Third Party Interference. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011530.

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Satellite monitoring offers unique advantages to the industry in meeting the objectives of managing third-party encroachment to mitigate the potential of mechanical damage. Satellite monitoring of third-party encroachment involves a persistent acquisition of satellite imagery over a pipeline right-of-way (ROW), combined with computerized change detection to identify potentially hazardous activities. Monitoring using satellite synthetic aperture radar (SAR) provides an all-weather day or night monitoring of a specific geographic location. This monitoring service can be enhanced with third-party
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Duan, Jingwei, Jie Yu, Qiangrong Zhai, and Qingbian Ma. Survival and Neurologic Outcome of Different Time of Collapse to return of Spontaneous Circulation in Cardiac Arrest with Targeted Temperature Management: a Bayesian Network Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.8.0027.

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Nagel, Jessica, Holly Paisted, Christopher Peck, and Paul Duffy. Condition and trends of estuarine water quality in George Washington Birthplace National Monument: Northeast Coastal and Barrier Network, 2009-2021. National Park Service, 2025. https://doi.org/10.36967/2311158.

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This report summarizes conditions and trends in routine long-term water quality monitoring data collected at George Washington Birthplace National Monument in Virginia from 2009 through 2021 as part of the NPS NCBN Vital Signs Estuarine Nutrient Enrichment monitoring program. This comprehensive program incorporates spatial surveys, repeated monitoring at trend stations, and continuous monitoring at a fixed logging station in an effort to establish overall condition of the estuary as well as to identify potential trends or changes in condition over time. All monitoring was conducted during a fo
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Nagel, Jessica, Holly Paisted, Christopher Peck, and Paul Duffy. Condition and trends of estuarine water quality in Colonial National Historical Park: Northeast Coastal and Barrier Network, 2008-2022. National Park Service, 2025. https://doi.org/10.36967/2310814.

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This report summarizes conditions and trends in routine long-term water quality monitoring data collected at Colonial National Historical Park in Virginia from 2008 through 2022 as part of the NPS NCBN Vital Signs Estuarine Nutrient Enrichment monitoring program. This comprehensive program incorporates spatial surveys, repeated monitoring at trend stations, and continuous monitoring at a fixed logging station in an effort to establish overall condition of the estuary as well as to identify potential trends or changes in condition over time. All monitoring was conducted biennially during a four
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Nagel, Jessica, Holly Plaisted, Christopher Peck, and Paul Duffy. Condition and trends of estuarine water quality and seagrass in Assateague Island National Seashore: Northeast Coastal and Barrier Network, 2005-2022. National Park Service, 2025. https://doi.org/10.36967/2310561.

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This report summarizes conditions and trends in routine long-term water quality monitoring and seagrass data collected at Assateague Island National Seashore in Maryland and Virginia from 2005 through 2022 as part of the NPS NCBN Vital Signs Estuarine Nutrient Enrichment monitoring program. This comprehensive program incorporates spatial surveys, repeated monitoring at trend stations, and continuous monitoring at a fixed logging station in an effort to establish overall water quality condition of the estuary as well as to identify potential trends or changes in condition over time. All monitor
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Nagel, Jessica, Holly Paisted, Christopher Peck, and Paul Duffy. Condition and trends of estuarine water quality in Gateway National Recreation Area: Northeast Coastal and Barrier Network, 2003–2022. National Park Service, 2025. https://doi.org/10.36967/2310870.

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This report summarizes conditions and trends in routine long-term water quality monitoring data collected at Gateway National Recreation Area in New York from 2003 through 2022 as part of the NPS NCBN Vital Signs Estuarine Nutrient Enrichment monitoring program. This comprehensive program incorporates spatial surveys, repeated monitoring at trend stations, and continuous monitoring at a fixed logging station in an effort to establish overall condition of the estuary as well as to identify potential trends or changes in condition over time. All monitoring was conducted during a four-week summer
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Nagel, Jessica, Holly Paisted, Christopher Peck, and Paul Duffy. Condition and trends of estuarine water quality and seagrass in Fire Island National Seashore: Northeast Coastal and Barrier Network, 2003–2022. National Park Service, 2025. https://doi.org/10.36967/2311455.

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This report summarizes conditions and trends in routine long-term water quality monitoring and seagrass data collected at Fire Island National Seashore in New York from 2003 through 2022 as part of the NPS NCBN Vital Signs Estuarine Nutrient Enrichment monitoring program. This comprehensive program incorporates spatial surveys, repeated monitoring at trend stations, and continuous monitoring at a fixed logging station in an effort to establish overall water quality condition of the estuary as well as to identify potential trends or changes in condition over time. All monitoring was conducted d
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