Relevant bibliographies by topics / Belief approximation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Belief approximation'

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

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Journal articles on the topic "Belief approximation"

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Leike, Reimar, and Torsten Enßlin. "Optimal Belief Approximation." Entropy 19, no. 8 (2017): 402. http://dx.doi.org/10.3390/e19080402.

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Guo, Yuanzhen, Hao Xiong, and Nicholas Ruozzi. "Marginal Inference in Continuous Markov Random Fields Using Mixtures." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7834–41. http://dx.doi.org/10.1609/aaai.v33i01.33017834.

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Exact marginal inference in continuous graphical models is computationally challenging outside of a few special cases. Existing work on approximate inference has focused on approximately computing the messages as part of the loopy belief propagation algorithm either via sampling methods or moment matching relaxations. In this work, we present an alternative family of approximations that, instead of approximating the messages, approximates the beliefs in the continuous Bethe free energy using mixture distributions. We show that these types of approximations can be combined with numerical quadra
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WEILER, THOMAS. "APPROXIMATION OF BELIEF FUNCTIONS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 06 (2003): 749–77. http://dx.doi.org/10.1142/s021848850300251x.

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This paper addresses the approximation of belief functions by probability functions where the approximation is based on minimizing the Euclidean distance. First of all, we simplify this optimization problem so it becomes equivalent to a standard problem in linear algebra. For the simplified optimization problem, we provide the analytic solution. Furthermore, we show that for Dempster-Shafer belief the simplified optimization problem is equivalent to the original one. In terms of semantics, we compare the approximation of belief functions to various alternative approaches, e.g. pignistic transf
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Zhang, Yan-Lan, and Chang-Qing Li. "Interpretations of belief functions in approximation operators by covering." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 5903–13. http://dx.doi.org/10.3233/jifs-201887.

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The rough set theory and the evidence theory are two important methods used to deal with uncertainty. The relationships between the rough set theory and the evidence theory have been discussed. In covering rough set theory, several pairs of covering approximation operators are characterized by belief and plausibility functions. The purpose of this paper is to review and examine interpretations of belief functions in covering approximation operators. Firstly, properties of the belief structures induced by two pairs of covering approximation operators are presented. Then, for a belief structure
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Gormley, Matthew R., Mark Dredze, and Jason Eisner. "Approximation-Aware Dependency Parsing by Belief Propagation." Transactions of the Association for Computational Linguistics 3 (December 2015): 489–501. http://dx.doi.org/10.1162/tacl_a_00153.

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We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O( n3) runtime. It outputs the parse with maximum expected recall—but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this grad
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Bhattacharyya, C., and S. S. Keerthi. "Mean Field Methods for a Special Class of Belief Networks." Journal of Artificial Intelligence Research 15 (August 1, 2001): 91–114. http://dx.doi.org/10.1613/jair.734.

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The chief aim of this paper is to propose mean-field approximations for a broad class of Belief networks, of which sigmoid and noisy-or networks can be seen as special cases. The approximations are based on a powerful mean-field theory suggested by Plefka. We show that Saul, Jaakkola and Jordan' s approach is the first order approximation in Plefka's approach, via a variational derivation. The application of Plefka's theory to belief networks is not computationally tractable. To tackle this problem we propose new approximations based on Taylor series. Small scale experiments show that the prop
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Straszak, Damian, and Nisheeth K. Vishnoi. "Belief Propagation, Bethe Approximation and Polynomials." IEEE Transactions on Information Theory 65, no. 7 (2019): 4353–63. http://dx.doi.org/10.1109/tit.2019.2901854.

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Cevolani, G. "Truth approximation via abductive belief change." Logic Journal of IGPL 21, no. 6 (2013): 999–1016. http://dx.doi.org/10.1093/jigpal/jzt010.

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Schwöbel, Sarah, Stefan Kiebel, and Dimitrije Marković. "Active Inference, Belief Propagation, and the Bethe Approximation." Neural Computation 30, no. 9 (2018): 2530–67. http://dx.doi.org/10.1162/neco_a_01108.

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When modeling goal-directed behavior in the presence of various sources of uncertainty, planning can be described as an inference process. A solution to the problem of planning as inference was previously proposed in the active inference framework in the form of an approximate inference scheme based on variational free energy. However, this approximate scheme was based on the mean-field approximation, which assumes statistical independence of hidden variables and is known to show overconfidence and may converge to local minima of the free energy. To better capture the spatiotemporal properties
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Cevolani, Gustavo. "Truth approximation, belief merging, and peer disagreement." Synthese 191, no. 11 (2014): 2383–401. http://dx.doi.org/10.1007/s11229-014-0486-2.

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Dissertations / Theses on the topic "Belief approximation"

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Sibel, Jean-Christophe. "Region-based approximation to solve inference in loopy factor graphs : decoding LDPC codes by the Generalized Belief Propagation." Phd thesis, Université de Cergy Pontoise, 2013. http://tel.archives-ouvertes.fr/tel-00905668.

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This thesis addresses the problem of inference in factor graphs, especially the LDPC codes, almost solved by message-passing algorithms. In particular, the Belief Propagation algorithm (BP) is investigated as a particular message-passing algorithm whose suboptimality is discussed in the case where the factor graph has a loop-like topology. From the equivalence between the BP and the Bethe approximation in statistical physics that is generalized to the region-based approximation, is detailed the Generalized Belief Propagation algorithm (GBP), a message-passing algorithm between clusters of the
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Matcha, Chaitanya Kumar, Mohsen Bahrami, Shounak Roy, Shayan Garani Srinivasa, and Bane Vasic. "Generalized belief propagation based TDMR detector and decoder." IEEE, 2016. http://hdl.handle.net/10150/622831.

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Two dimensional magnetic recording (TDMR) achieves high areal densities by reducing the size of a bit comparable to the size of the magnetic grains resulting in two dimensional (2D) inter symbol interference (ISI) and very high media noise. Therefore, it is critical to handle the media noise along with the 2D ISI detection. In this paper, we tune the generalized belief propagation (GBP) algorithm to handle the media noise seen in TDMR. We also provide an intuition into the nature of hard decisions provided by the GBP algorithm. The performance of the GBP algorithm is evaluated over a Voronoi b
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Samet, Asma. "Classifier ensemble under the belief function framework." Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0203.

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Dans cette thèse, nous nous intéressons au problème de construction d’ensemble de classifieurs pour le traitement de données incertaines, plus particulièrement les données modélisées avec la théorie des fonctions de croyance. Dans un premier temps, nous introduisons de nouveaux algorithmes d’apprentissage dans le cadre évidentiel. Par la suite, nous abordons le processus de construction d’ensemble de classifieurs qui se fonde sur deux étapes importantes : la sélection des classifieurs individuels et la fusion des classifieurs. Pour l’étape de sélection, la diversité entre les classifieurs indi
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Harris, William John Ebenezer. "Entre fronteiras e discursos: um estudo comparado sobre o processo de integração física entre Brasil-Guyana e México-Belize." Universidade Federal de Roraima, 2016. http://www.bdtd.ufrr.br/tde_busca/arquivo.php?codArquivo=368.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Esta dissertação levanta uma análise comparativa dos processos políticos que antecederam a construção de duas pontes transnacionais nas Américas: a ponte sobre o Rio Tacutu, que liga as cidades de Lethem (Região 9, Guyana) e Bonfim (Roraima, Brasil); e a ponte sobre o Rio Hondo, que liga as cidades de Santa Elena (Corozal, Belize) e Subteniente López (Quintana Roo, México). Enquanto isso, o estudo inserese no corpo mais amplo de pesquisas que analisam o processo de integração das Américas através do uso de infraestrutura física. Al
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Saade, Alaa. "Spectral inference methods on sparse graphs : theory and applications." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE024/document.

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Face au déluge actuel de données principalement non structurées, les graphes ont démontré, dans une variété de domaines scientifiques, leur importance croissante comme language abstrait pour décrire des interactions complexes entre des objets complexes. L’un des principaux défis posés par l’étude de ces réseaux est l’inférence de propriétés macroscopiques à grande échelle, affectant un grand nombre d’objets ou d’agents, sur la seule base des interactions microscopiquesqu’entretiennent leurs constituants élémentaires. La physique statistique, créée précisément dans le but d’obtenir les lois mac
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Jayasooriya, Jayasooriya Arachchige Sachini Nisansala. "Multi-edge type density evolution: analysis, code optimization and applications to raptor code design." Thesis, 2017. http://hdl.handle.net/1959.13/1343092.

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Research Doctorate - Doctor of Philosophy (PhD)<br>The field of error correcting codes has been revolutionized by the introduction of graph-based codes, such as low-density parity-check (LDPC) codes and turbo codes. These codes exhibit dramatic performance improvement with significantly lower decoding complexity over previously developed coding schemes such as Hamming codes and BCH codes, which are overwhelmingly algebraic. Since then, understanding the performance of these codes, and using this understanding to design capacity-approaching codes, is one of the utmost subjects of interest among
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Books on the topic "Belief approximation"

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Staffel, Julia. Unsettled Thoughts. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198833710.001.0001.

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How should thinkers cope with uncertainty? What makes their degrees of belief rational, and how should they reason about uncertain matters? Recent research in epistemology has attempted to answer these questions by developing formal models of ideally rational credences. However, we know from psychological research that ideal rationality is unattainable for human thinkers. This raises the question of how rational ideals can apply to human thinkers. A popular reply is that the more a thinker’s imperfectly rational credences approximate compliance with norms of ideal rationality, the better. But
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Morawetz, Klaus. Systems with Condensates and Pairing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797241.003.0012.

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The Bose–Einstein condensation and appearance of superfluidity and superconductivity are introduced from basic phenomena. A systematic theory based on the asymmetric expansion of chapter 11 is shown to correct the T-matrix from unphysical multiple-scattering events. The resulting generalised Soven scheme provides the Beliaev equations for Boson’s and the Nambu–Gorkov equations for fermions without the usage of anomalous and non-conserving propagators. This systematic theory allows calculating the fluctuations above and below the critical parameters. Gap equations and Bogoliubov–DeGennes equati
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Book chapters on the topic "Belief approximation"

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Kuipers, Theo A. F. "Dovetailing Belief Base Revision with Truth Approximation." In Nomic Truth Approximation Revisited. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98388-2_15.

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Bork, Alexander, Joost-Pieter Katoen, and Tim Quatmann. "Under-Approximating Expected Total Rewards in POMDPs." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_2.

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AbstractWe consider the problem: is the optimal expected total reward to reach a goal state in a partially observable Markov decision process (POMDP) below a given threshold? We tackle this—generally undecidable—problem by computing under-approximations on these total expected rewards. This is done by abstracting finite unfoldings of the infinite belief MDP of the POMDP. The key issue is to find a suitable under-approximation of the value function. We provide two techniques: a simple (cut-off) technique that uses a good policy on the POMDP, and a more advanced technique (belief clipping) that
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Jiroušek, Radim. "Approximation of Data by Decomposable Belief Models." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14055-6_5.

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Kuipers, Theo A. F. "Basic and Refined Nomic Truth Approximation by Evidence-Guided Belief Set Revision." In Nomic Truth Approximation Revisited. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98388-2_14.

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Kuipers, Theo A. F. "Dovetailing Belief Base Revision with (Basic) Truth Approximation." In Logic, Argumentation & Reasoning. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9011-6_5.

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Weiler, Thomas, and Ulrich Bodenhofer. "Approximation of Belief Functions by Minimizing Euclidean Distances." In Advances in Intelligent and Soft Computing. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1773-7_16.

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Joshi, A. V., S. C. Sahasrabudhe, and K. Shankar. "Bayesian approximation and invariance of Bayesian belief functions." In Symbolic and Quantitative Approaches to Reasoning and Uncertainty. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60112-0_29.

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Na, Chongning, Hui Wang, Dragan Obradovic, and Uwe D. Hanebeck. "Fourier Density Approximation for Belief Propagation in Wireless Sensor Networks." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89859-7_25.

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Yaghlane, Amel Ben, Thierry Denœux, and Khaled Mellouli. "Coarsening Approximations of Belief Functions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44652-4_32.

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Meseguer Velasco, Silvia. "The Legal Protection of Religious Freedom and Belief in Public Transport: An Approximation from the Case Law of the Strasbourg Court." In Protection and Promotion of Freedom of Religions and Beliefs in the European Context. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34503-6_4.

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Conference papers on the topic "Belief approximation"

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Straszak, Damian, and Nisheeth K. Vishnoi. "Belief propagation, bethe approximation and polynomials." In 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2017. http://dx.doi.org/10.1109/allerton.2017.8262801.

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Liu, Dong, Nima N. Moghadam, Lars K. Rasmussen, Jinliang Huang та Saikat Chatterjee. "α Belief Propagation as Fully Factorized Approximation". У 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969545.

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Gogu, Christian, Youchun Qiu, Stephane Segonds, and Christian Bes. "Belief and plausibility approximation methods for multidimensional functions." In 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-1842.

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Nordlof, Jonas, Gustaf Hendeby, and Daniel Axehill. "Improved Virtual Landmark Approximation for Belief-Space Planning." In 2021 IEEE 24th International Conference on Information Fusion (FUSION). IEEE, 2021. http://dx.doi.org/10.23919/fusion49465.2021.9626933.

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Na, Chongning, Hui Wang, Dragan Obradovic, and Uwe D. Hanebeck. "Fourier density approximation for belief propagation in wireless sensor networks." In 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008). IEEE, 2008. http://dx.doi.org/10.1109/mfi.2008.4648080.

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Balakrishnan, H., Inseok Hwang, and C. J. Tomlin. "Polynomial approximation algorithms for belief matrix maintenance in identity management." In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1429569.

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Denoeux, Thierry, and Orakanya Kanjanatarakul. "Multistep Prediction using Point-Cloud Approximation of Continuous Belief Functions." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858988.

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Lian, Mengke, and Henry D. Pfister. "Belief-propagation reconstruction for compressed sensing: Quantization vs. Gaussian approximation." In 2015 53rd Annual Allerton Conference on Communication, Control and Computing (Allerton). IEEE, 2015. http://dx.doi.org/10.1109/allerton.2015.7447132.

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Wei, Qing, Xinde Li, and Jean Dezert. "Approximation of Basic Belief Assignment Based on Focal Element Compatibility." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011173.

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Lim, Michael, Tyler Becker, Mykel Kochenderfer, Claire Tomlin, and Zachary Sunberg. "Optimality Guarantees for Particle Belief Approximation of POMDPs (Abstract Reprint)." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/953.

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Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Ou
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Reports on the topic "Belief approximation"

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Pazzan, Michael J., and Rina Dechter. Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada383161.

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