Relevant bibliographies by topics / Conditional Evidential Network

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  3. Conference papers

Academic literature on the topic 'Conditional Evidential Network'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Conditional Evidential Network"

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Zuo, Lin, Tangfan Xiahou, and Yu Liu. "Evidential network-based failure analysis for systems suffering common cause failure and model parameter uncertainty." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 6 (2018): 2225–35. http://dx.doi.org/10.1177/0954406218781407.

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The fault tree analysis has been extensively implemented in failure analysis of engineered systems. In most cases, the probabilities of basic events, e.g. components’ failures, are represented by crisp values in the fault tree analyses. However, due to lack of knowledge, scarcity of failure data, or vague judgments from experts, it may produce parameter uncertainty associated with degradation models of components/systems, and such model parameter uncertainty can be quantified by the epistemic uncertainty. In addition, the common cause failure, related to the simultaneous failures of two or mor
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Dunham, Joel, Eric Johnson, Eric Feron, and Brian German. "Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs." Sensors 20, no. 13 (2020): 3727. http://dx.doi.org/10.3390/s20133727.

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Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships
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Pei, Dong, Daguo Qin, Yang Sun, Guangzhi Bu, and Zhonghua Yao. "Prioritization Assessment for Capability Gaps in Weapon System of Systems Based on the Conditional Evidential Network." Applied Sciences 8, no. 2 (2018): 265. http://dx.doi.org/10.3390/app8020265.

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Bougofa, M., A. Bouafia, A. Baziz, S. Aberkane, R. Kharzi, and A. Bellaouar. "Risk analysis-based reliability assessment approach under epistemic uncertainty using a dynamic evidential network." IOP Conference Series: Earth and Environmental Science 896, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1755-1315/896/1/012035.

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Abstract Probabilistic modeling is widely used in industrial practices, particularly for assessing complex systems’ safety, risk analysis, and reliability. Conventional risk analysis methodologies generally have a limited ability to deal with dependence, failure behavior, and epistemic uncertainty such as parameter uncertainty. This work proposes a risk-based reliability assessment approach using a dynamic evidential network (DEN). The proposed model integrates Dempster-Shafer theory (DST) for describing parameter uncertainty with a dynamic Bayesian network (DBN) for dependency representation
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Boualem, Adda, Marwane Ayaida, Cyril De Runz, and Youcef Dahmani. "An Evidential Approach for Area Coverage in Mobile Wireless Sensor Networks." International Journal of Fuzzy System Applications 10, no. 3 (2021): 30–54. http://dx.doi.org/10.4018/ijfsa.2021070103.

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The study of coverage problem in uncertain WSN environment requires the consideration of this uncertainty by taking the best possible decisions, since it is impossible to explicitly represent all the combinatorics to produce a conditional active/passive state nodes' planning in the area of interest, and allow reasoning on various environmental states of the partially known physical world. This paper addresses the problem of area coverage based on the Dempster-Shafer theory. The authors aim to ensure the full area coverage while using a subset of connected nodes, with minimal costs using a mini
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Munro, Neil Allan Robertson. "Should Medical Experts Giving Evidence in Criminal Trials Adhere to EFNSI Forensic Guidelines in Evaluative Reporting." Forensic Sciences 5, no. 1 (2025): 13. https://doi.org/10.3390/forensicsci5010013.

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Miscarriages of justice led to concerns that forensic science reports were prosecution-biassed and led to elementary errors of probability. The European Network of Forensic Science Institutes (EFNSI) and other institutes developed standards requiring reporting of the probability of evidence under all hypotheses (usually prosecution and defence hypotheses) with the likelihood ratio (LR). LR=pEHppEHd, values > 1, being probative for a prosecution hypothesis. In elementary two-variable conditional probability theory (Baye’s theorem), the LR is also an updating factor which multiplies the odds
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Xu, Hong, and Philippe Smets. "Reasoning in evidential networks with conditional belief functions." International Journal of Approximate Reasoning 14, no. 2-3 (1996): 155–85. http://dx.doi.org/10.1016/0888-613x(96)00113-2.

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Chinese, Journal of Aeronautics. "A Reasoning Method in Conditional Evidential Networks based on Dezert-Smarandache Model." May 5, 2015. https://doi.org/10.5281/zenodo.22926.

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Aiming to solving the problem that the evidence information based on Dezert-Smarandache (DSm) model can not be fused effectively in Conditional Evidential Network based on Smets/DS model (ENC), a reasoning method in Conditional Evidential Network based on DSm model is proposed. First, the conditional reasoning formular in Conditional Evidential Network based on DSm model is proved and the reasoning algorithm is proposed. Then, the hyperpower set of overlapping hypotheses is refined to a new power set of exhaustive and exclusive hypotheses, and the conditional belief functio
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Grim, Patrick, Frank Seidl, Calum McNamara, Isabell N. Astor, and Caroline Diaso. "The punctuated equilibrium of scientific change: a Bayesian network model." Synthese 200, no. 4 (2022). http://dx.doi.org/10.1007/s11229-022-03720-z.

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AbstractOur scientific theories, like our cognitive structures in general, consist of propositions linked by evidential, explanatory, probabilistic, and logical connections. Those theoretical webs ‘impinge on the world at their edges,’ subject to a continuing barrage of incoming evidence (Quine 1951, 1953). Our credences in the various elements of those structures change in response to that continuing barrage of evidence, as do the perceived connections between them. Here we model scientific theories as Bayesian nets, with credences at nodes and conditional links between them modelled as condi
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Long, Chengfeng, Xingxin Liu, Yakun Yang, et al. "A data fusion method in wireless sensor network based on belief structure." EURASIP Journal on Wireless Communications and Networking 2021, no. 1 (2021). http://dx.doi.org/10.1186/s13638-021-01916-4.

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AbstractConsidering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction ar
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Book chapters on the topic "Conditional Evidential Network"

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Fricoteaux, Loïc, Indira Thouvenin, Jérôme Olive, and Paul George. "Evidential Network with Conditional Belief Functions for an Adaptive Training in Informed Virtual Environment." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29461-7_49.

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Yaghlane, Boutheina Ben, Philippe Smets, and Khaled Mellouli. "Directed Evidential Networks with Conditional Belief Functions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45062-7_24.

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Ben Hariz, Narjes, and Boutheina Ben Yaghlane. "Learning Parameters in Directed Evidential Networks with Conditional Belief Functions." In Belief Functions: Theory and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11191-9_32.

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Laâmari, Wafa, and Boutheina Ben Yaghlane. "Reasoning in Singly-Connected Directed Evidential Networks with Conditional Beliefs." In Artificial Intelligence: Methods and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07064-3_18.

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Laâmari, Wafa, Boutheina Ben Yaghlane, and Christophe Simon. "New Propagation Algorithm in Dynamic Directed Evidential Networks with Conditional Belief Functions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39515-4_5.

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Laâmari, Wafa, Boutheina Ben Yaghlane, and Christophe Simon. "Dynamic Directed Evidential Networks with Conditional Belief Functions: Application to System Reliability." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31718-7_50.

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Laâmari, Wafa, Narjes Ben Hariz, and Boutheina Ben Yaghlane. "Approximate Inference in Directed Evidential Networks with Conditional Belief Functions Using the Monte Carlo Algorithm." In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08852-5_50.

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Laâmari, Wafa, Boutheina Ben Yaghlane, and Christophe Simon. "On the Complexity of the Graphical Representation and the Belief Inference in the Dynamic Directed Evidential Networks with Conditional Belief Functions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33362-0_16.

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Laâmari, Wafa, Boutheina Ben Yaghlane, and Christophe Simon. "On the Use of a Mixed Binary Join Tree for Exact Inference in Dynamic Directed Evidential Networks with Conditional Belief Functions." In Knowledge Science, Engineering and Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39787-5_26.

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Conference papers on the topic "Conditional Evidential Network"

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Dong, Pei, and Qin Daguo. "Evaluation of contribution rate of weapon equipment system of systems capability based on conditional evidential network." In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2017. http://dx.doi.org/10.1109/icsess.2017.8342955.

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Qiang Guo, F. Smarandache, Hai-peng Wang, Shi-you Xu, and Xian Li. "A Reasoning Method in Conditional Evidential Networks based on DSm-PCR5." In 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015). Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/cp.2015.0946.

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