Auswahl der wissenschaftlichen Literatur zum Thema „Von Mises-Fisher prior“
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Zeitschriftenartikel zum Thema "Von Mises-Fisher prior"
Ma, He, und Weipeng Wu. „A deep clustering framework integrating pairwise constraints and a VMF mixture model“. Electronic Research Archive 32, Nr. 6 (2024): 3952–72. http://dx.doi.org/10.3934/era.2024177.
Der volle Inhalt der QuelleMichel, Nicolas, Giovanni Chierchia, Romain Negrel und Jean-François Bercher. „Learning Representations on the Unit Sphere: Investigating Angular Gaussian and Von Mises-Fisher Distributions for Online Continual Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 13 (24.03.2024): 14350–58. http://dx.doi.org/10.1609/aaai.v38i13.29348.
Der volle Inhalt der QuelleCao, Mingxuan, Kai Xie, Feng Liu, Bohao Li, Chang Wen, Jianbiao He und Wei Zhang. „Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network“. Sensors 23, Nr. 6 (22.03.2023): 3355. http://dx.doi.org/10.3390/s23063355.
Der volle Inhalt der QuelleFang, Jinyuan, Shangsong Liang, Zaiqiao Meng und Maarten De Rijke. „Hyperspherical Variational Co-embedding for Attributed Networks“. ACM Transactions on Information Systems 40, Nr. 3 (31.07.2022): 1–36. http://dx.doi.org/10.1145/3478284.
Der volle Inhalt der QuelleHornik, Kurt, und Bettina Grün. „On conjugate families and Jeffreys priors for von Mises–Fisher distributions“. Journal of Statistical Planning and Inference 143, Nr. 5 (Mai 2013): 992–99. http://dx.doi.org/10.1016/j.jspi.2012.11.003.
Der volle Inhalt der QuelleLewin, Peter. „Rothbard and Mises on Interest: An Exercise in Theoretical Purity“. Journal of the History of Economic Thought 19, Nr. 1 (1997): 141–59. http://dx.doi.org/10.1017/s1053837200004727.
Der volle Inhalt der QuelleAndreella, Angela, und Livio Finos. „Procrustes Analysis for High-Dimensional Data“. Psychometrika, 18.05.2022. http://dx.doi.org/10.1007/s11336-022-09859-5.
Der volle Inhalt der QuelleNakhaei Rad, Najmeh, Andriette Bekker, Mohammad Arashi und Christophe Ley. „Coming Together of Bayesian Inference and Skew Spherical Data“. Frontiers in Big Data 4 (08.02.2022). http://dx.doi.org/10.3389/fdata.2021.769726.
Der volle Inhalt der QuelleDissertationen zum Thema "Von Mises-Fisher prior"
Hornik, Kurt, und Bettina Grün. „On conjugate families and Jeffreys priors for von Mises-Fisher distributions“. Elsevier, 2013. http://dx.doi.org/10.1016/j.jspi.2012.11.003.
Der volle Inhalt der QuelleTraullé, Benjamin. „Techniques d’échantillonnage pour la déconvolution aveugle bayésienne“. Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0004.
Der volle Inhalt der QuelleThese thesis works address two main challenges in the field of Bayesian blind deconvolution using Markov chain Monte Carlo (MCMC) methods. Firstly, in Bayesian blind deconvolution, it is common to use Gaussian-type priors. However, these priors do not solve the scale ambiguity problem. The latter poses difficulties in the convergence of classical MCMC algorithms, which exhibit slow scale sampling, and complicates the design of scale-free estimators. To overcome this limitation, a von Mises–Fisher prior is proposed, which alleviates the scale ambiguity. This approach has already demonstrated its regularization effect in other inverse problems, including optimization-based blind deconvolution. The advantages of this prior within MCMC algorithms are discussed compared to conventional Gaussian priors, both theoretically and experimentally, especially in low dimensions. However, the multimodal nature of the posterior distribution still poses challenges and decreases the quality of the exploration of the state space, particularly when using algorithms such as the Gibbs sampler. These poor mixing properties lead to suboptimal performance in terms of inter-mode and intra-mode exploration and can limit the usefulness of Bayesian estimators at this stage. To address this issue, we propose an original approach based on the use of a reversible jump MCMC (RJMCMC) algorithm, which significantly improves the exploration of the state space by generating new states in high probability regions identified in a preliminary stage. The effectiveness of the RJMCMC algorithm is empirically demonstrated in the context of highly multimodal posteriors, particularly in low dimensions, for both Gaussian and von Mises–Fisher priors. Furthermore, the observed behavior of RJMCMC in increasing dimensions provides support for the applicability of this approach for sampling multimodal distributions in the context of Bayesian blind deconvolution
Konferenzberichte zum Thema "Von Mises-Fisher prior"
Traulle, Benjamin, Stephanie Bidon und Damien Roque. „A von Mises—Fisher prior to Remove Scale Ambiguity in Blind Deconvolution“. In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909710.
Der volle Inhalt der QuelleЧерняев, Сергей, Sergey Chernyaev, Олег Лукашенко und Oleg Lukashenko. „Comparative Analysis of Methods for Segmentation of FMRI Images Based on Markov Random Fields“. In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-143-147.
Der volle Inhalt der QuelleJin, Yujie, Xu Chu, Yasha Wang und Wenwu Zhu. „Domain Generalization through the Lens of Angular Invariance“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/139.
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