Relevant bibliographies by topics / Basic Belief Assignments

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Academic literature on the topic 'Basic Belief Assignments'

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

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Journal articles on the topic "Basic Belief Assignments"

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Dezert, Jean, and Albena Tchamova. "Involutory Negator of Basic Belief Assignments." Cybernetics and Information Technologies 23, no. 3 (2023): 3–22. http://dx.doi.org/10.2478/cait-2023-0021.

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Abstract This paper analyzes the different definitions of a negator of a probability mass function (pmf) and a Basic Belief Assignment (BBA) available in the literature. To overcome their limitations we propose an involutory negator of BBA, and we present a new indirect information fusion method based on this negator which can simplify the conflict management problem. The direct and indirect information fusion strategies are analyzed for three interesting examples of fusion of two BBAs. We also propose two methods for using the whole available information (the original BBAs and their negators)
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Li, Meizhu, Xi Lu, Qi Zhang, and Yong Deng. "Multiscale Probability Transformation of Basic Probability Assignment." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/319264.

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Decision making is still an open issue in the application of Dempster-Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability assignments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation. In the multiscale probability function, a factorqbased on the Tsallis entropy is used to make the
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Zhao, Yonggang, Duofa Ji, Xiaodong Yang, Liguo Fei, and Changhai Zhai. "An Improved Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Deng Entropy and Belief Interval." Entropy 21, no. 11 (2019): 1122. http://dx.doi.org/10.3390/e21111122.

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It is still an open issue to measure uncertainty of the basic probability assignment function under Dempster-Shafer theory framework, which is the foundation and preliminary work for conflict degree measurement and combination of evidences. This paper proposes an improved belief entropy to measure uncertainty of the basic probability assignment based on Deng entropy and the belief interval, which takes the belief function and the plausibility function as the lower bound and the upper bound, respectively. Specifically, the center and the span of the belief interval are employed to define the to
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Broniatowski, Michel, and Wolfgang Stummer. "Some Theoretical Foundations of Bare-Simulation Optimization of Some Directed Distances between Fuzzy Sets Respective Basic Belief Assignments." Entropy 26, no. 4 (2024): 312. http://dx.doi.org/10.3390/e26040312.

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It is well known that in information theory—as well as in the adjacent fields of statistics, machine learning and artificial intelligence—it is essential to quantify the dissimilarity between objects of uncertain/imprecise/inexact/vague information; correspondingly, constrained optimization is of great importance, too. In view of this, we define the dissimilarity-measure-natured generalized φ–divergences between fuzzy sets, ν–rung orthopair fuzzy sets, extended representation type ν–rung orthopair fuzzy sets as well as between those fuzzy set types and vectors. For those, we present how to tac
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Ennaceur, Amel, Zied Elouedi, and Eric Lefevre. "Belief AHP Method — AHP Method with the Belief Function Framework." International Journal of Information Technology & Decision Making 15, no. 03 (2016): 553–73. http://dx.doi.org/10.1142/s0219622016500139.

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In this paper, the analytic hierarchy process (AHP) method is extended to an uncertain environment where the uncertainty is represented by belief functions as interpreted in the transferable belief model (TBM). Our proposed approach, called belief AHP, is developed to help the decision maker to determine what the best alternatives are, considering multiple conflicting criteria where both alternatives and criteria may be soiled with imperfection. The Belief AHP method aims at comparing subsets of criteria and groups of alternatives in order to reduce the pair-wise comparisons number. Furthermor
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Pan, Lipeng, and Yong Deng. "A New Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Belief Function and Plausibility Function." Entropy 20, no. 11 (2018): 842. http://dx.doi.org/10.3390/e20110842.

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How to measure the uncertainty of the basic probability assignment (BPA) function is an open issue in Dempster–Shafer (D–S) theory. The main work of this paper is to propose a new belief entropy, which is mainly used to measure the uncertainty of BPA. The proposed belief entropy is based on Deng entropy and probability interval consisting of lower and upper probabilities. In addition, under certain conditions, it can be transformed into Shannon entropy. Numerical examples are used to illustrate the efficiency of the new belief entropy in measurement uncertainty.
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SMARANDACHE, FLORENTIN, and JEAN DEZERT. "UNIFORM AND PARTIALLY UNIFORM REDISTRIBUTION RULES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, no. 06 (2011): 921–37. http://dx.doi.org/10.1142/s0218488511007404.

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This paper introduces two new fusion rules for combining quantitative basic belief assignments. These rules although very simple have not been proposed in literature so far and could serve as useful alternatives because of their low computation cost with respect to the recent advanced Proportional Conflict Redistribution rules developed in the DSmT framework.
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Wang, Zhi Song, Li Wei Tang, Wen Wen Yu, and Jin Hua Cao. "Antiaircraft Gun Automatic Fusion Diagnosis Based on D-S Evidence Theory." Applied Mechanics and Materials 241-244 (December 2012): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.288.

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Antiaircraft gun automatic is irrotational machine, its motion presents characteristic of stage, so we proposed a fault diagnosis fusion model based on Dempster-Shafer (D-S) evidence theory. At first, feature parameters are extracted from test data of multi-sensor, then, we propose a revised Minkowski distance to create evidences. Finally, we fuse basic belief assignments according to Dempster combination rule, and the analysis result verifies the effectiveness and feasibility of proposed fault diagnosis method.
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Ma, Mengmeng, and Jiyao An. "Combination of Evidence with Different Weighting Factors: A Novel Probabilistic-Based Dissimilarity Measure Approach." Journal of Sensors 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/509385.

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To solve the invalidation problem of Dempster-Shafer theory of evidence (DS) with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs) to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs) but al
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Chen, Yujie, Zexi Hua, Yongchuan Tang, and Baoxin Li. "Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy." Entropy 24, no. 8 (2022): 1164. http://dx.doi.org/10.3390/e24081164.

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Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source information, in this paper, we propose a multi-source information fusion method based on the Dempster-Shafer (DS) evidence theory with the negation of reconstructed basic probability assignments (nrBPA). To determine the initial basic probability assignment (BPA), the Gaussian distribut
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Books on the topic "Basic Belief Assignments"

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Fischer, Ronald. Human Essences and Cultural Embeddedness. Edited by Martijn van Zomeren and John F. Dovidio. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190247577.013.20.

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This chapter examines biology and culture as interactive influences in shaping the human essence, arguing that “culture is biological.” In particular, it considers the idea that cultural embeddedness might be a distinct human attribute from an evolutionary perspective. It then shows how biological processes shaped over millennia have culminated in a species that has the ability to read and write, play computer games, and fly airplanes. It also explores the genes-physiology-neuroscience side of human behavior and the social-cultural-economic environment in which the behavior takes place, along
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Manieson, Victor. Accelerated Keyboard Musicianship. Noyam Publishers, 2021. http://dx.doi.org/10.38159/npub.eb20211001.

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Approaches towards the formal learning of piano playing with respect to musicianship is one that demands the understanding of musical concepts and their applications. Consequently, it requires the boldness to immerse oneself in performance situations while trusting one’s instincts. One needs only to cultivate an amazing ear and a good understanding of music theory to break down progressions “quickly”. Like an alchemist, one would have to pick their creative impulses from their musical toolbox, simultaneously compelling their fingers to coordinate with the brain and the music present to generat
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Book chapters on the topic "Basic Belief Assignments"

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Kramosil, Ivan. "Nonspecificity Degrees of Basic Probability Assignments." In Probabilistic Analysis of Belief Functions. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0587-7_7.

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Dezert, Jean, Albena Tchamova, and Deqiang Han. "Measure of Information Content of Basic Belief Assignments." In Belief Functions: Theory and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17801-6_12.

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Boujelben, Mohamed Ayman, Yves De Smet, Ahmed Frikha, and Habib Chabchoub. "The First Belief Dominance: A New Approach in Evidence Theory for Comparing Basic Belief Assignments." In Algorithmic Decision Theory. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04428-1_24.

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Dezert, Jean, and Florentin Smarandache. "Canonical Decomposition of Basic Belief Assignment for Decision-Making Support." In Modelling and Development of Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68527-0_7.

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Wang, Hongfei, Wen Jiang, Xinyang Deng, and Jie Geng. "A New Multi-source Information Fusion Method Based on Belief Divergence Measure and the Negation of Basic Probability Assignment." In Belief Functions: Theory and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88601-1_24.

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Woods, Michael, David Wiggins, and Dorothy Edgington. "Ramsey’s Test and Adams’s Hypothesis." In Conditionals. Oxford University PressOxford, 1997. http://dx.doi.org/10.1093/oso/9780198751267.003.0003.

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Abstract Ramsey describes someone deciding whether or not to accept Q, after accepting P, as making minimal revisions in his other beliefs. On what basis is such a hypothetical revision made? In order to answer this question, it will be useful, and more realistic, to take account of the fact that the acceptance or rejection of a belief is not an all-or-nothing matter, and this applies to the acceptance of ‘If P then Q’. In so far as someone has any opinion at all on whether or not P is the case, he will assign to Pa certain probability. Since an individual’s beliefs are systematically related,
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Sivia, D. S. "Assigning probabilities." In Data Analysis. Oxford University PressOxford, 2006. http://dx.doi.org/10.1093/oso/9780198568315.003.0005.

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Abstract In the preceding three chapters, we have seen how pdfs can be manipulated, with the sum and product rule of probability, to address data analysis problems; little was said, however, about their assignment in the first place. We now turn to this basic question. In addition to justifying the earlier use of the binomial, Gaussian and Poisson distributions, we will have our first encounter with the principle of maximum entropy. In Section 1.2, we outlined how Cox showed that any method of plausible reasoning, which satisfies elementary requirements of logical consistency, must be equivale
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García-Valcárcel, Ana, and Juanjo Mena. "In-Service Teachers' Use of ICT for the Promotion of Collaborative Professional Learning." In Research Anthology on Facilitating New Educational Practices Through Communities of Learning. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7294-8.ch015.

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Information and communication technologies (ICT) are often rendered as key tools in the promotion of teachers' collaborative learning. Their use enables teachers to complete assignments, solve problems, or create products together. The content of this chapter is based on the information published in a previous research study by the authors. In that study, they aimed at describing teachers' use of ICT towards collaboration from a triple perspective: what they believe (teachers' opinion), what they know (teachers' knowledge), and what they do (teachers' use). A questionnaire and interviews were
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García-Valcárcel, Ana, and Juanjo Mena. "In-Service Teachers' Use of ICT for the Promotion of Collaborative Professional Learning." In Global Implications of Emerging Technology Trends. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4944-4.ch008.

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Information and communication technologies (ICT) are often rendered as key tools in the promotion of teachers' collaborative learning. Their use enables teachers to complete assignments, solve problems, or create products together. The content of this chapter is based on the information published in a previous research study by the authors. In that study, they aimed at describing teachers' use of ICT towards collaboration from a triple perspective: what they believe (teachers' opinion), what they know (teachers' knowledge), and what they do (teachers' use). A questionnaire and interviews were
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Rödl, Florian. "Necessary Unity." In Philosophical Foundations of Private International Law. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780192858771.003.0002.

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Abstract Elaborating on arguments from Kant’s legal philosophy, the chapter argues that the concept of private rights leads to the idea of necessary unity in choice of law. Unity is achieved if legislators and courts of all states subject the articulation of conflict-of-law rules to the same idea of justice. The relevant idea of justice is neither the idea of comparatively more impairment of governmental interests nor an idea of ‘conflicts justice’. Rather, justice in choice of law is about reasonably assigning jurisdiction to prescribe private-law rules. It is argued that the principle for th
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Conference papers on the topic "Basic Belief Assignments"

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Hei, Xiaojie, Jiangbo Wang, Xiaoguang Qu, and Pengyu Qiu. "A Method for Small Current Grounding Fault Location Based on the Modified D-S Evidence Theory with Multi-Criteria Basic Belief Assignment Adjustment." In 2024 8th International Conference on Smart Grid and Smart Cities (ICSGSC). IEEE, 2024. https://doi.org/10.1109/icsgsc62639.2024.10813970.

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Diaz, J., M. Rifqi, and B. Bouchon-Meunier. "A Similarity Measure between Basic Belief Assignments." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301730.

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Dezert, Jean. "An Effective Measure of Uncertainty of Basic Belief Assignments." In 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841273.

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Dezert, Jean, and Frédéric Dambreville. "Cross-Entropy and Relative Entropy of Basic Belief Assignments." In 2023 26th International Conference on Information Fusion (FUSION). IEEE, 2023. http://dx.doi.org/10.23919/fusion52260.2023.10224207.

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Dezert, Jean, Florentin Smarandache, Albena Tchamova, and Deqiang Han. "Fast Fusion of Basic Belief Assignments Defined on a Dichotomous Frame of Discernment." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190547.

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Ben Ameur, R., D. Coquin, and L. Valet. "Influence of basic belief assignments construction on the behaviour of a fusion system for tree species recognition." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009728.

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Wei, Yang, and Fu Yaowen. "Constructing basic belief assignment from feature data." In 2013 Chinese Automation Congress (CAC). IEEE, 2013. http://dx.doi.org/10.1109/cac.2013.6775807.

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Zhang, Zhe, Deqiang Han, Jean Dezert, and Yi Yang. "Determination of basic belief assignment using fuzzy numbers." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009757.

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Lefevre, E., P. Vannoorenberghe, and O. Colot. "Using information criteria in Dempster-Shafer's basic belief assignment." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793230.

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Dambreville, F. "Example of continuous basic belief assignment in the DSmT paradigm." In 2005 7th International Conference on Information Fusion. IEEE, 2005. http://dx.doi.org/10.1109/icif.2005.1591956.

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