Literatura científica selecionada sobre o tema "Von Mises-Fisher prior"
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Artigos de revistas sobre o assunto "Von Mises-Fisher prior"
Ma, He, e Weipeng Wu. "A deep clustering framework integrating pairwise constraints and a VMF mixture model". Electronic Research Archive 32, n.º 6 (2024): 3952–72. http://dx.doi.org/10.3934/era.2024177.
Texto completo da fonteMichel, Nicolas, Giovanni Chierchia, Romain Negrel e 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, n.º 13 (24 de março de 2024): 14350–58. http://dx.doi.org/10.1609/aaai.v38i13.29348.
Texto completo da fonteCao, Mingxuan, Kai Xie, Feng Liu, Bohao Li, Chang Wen, Jianbiao He e Wei Zhang. "Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network". Sensors 23, n.º 6 (22 de março de 2023): 3355. http://dx.doi.org/10.3390/s23063355.
Texto completo da fonteFang, Jinyuan, Shangsong Liang, Zaiqiao Meng e Maarten De Rijke. "Hyperspherical Variational Co-embedding for Attributed Networks". ACM Transactions on Information Systems 40, n.º 3 (31 de julho de 2022): 1–36. http://dx.doi.org/10.1145/3478284.
Texto completo da fonteHornik, Kurt, e Bettina Grün. "On conjugate families and Jeffreys priors for von Mises–Fisher distributions". Journal of Statistical Planning and Inference 143, n.º 5 (maio de 2013): 992–99. http://dx.doi.org/10.1016/j.jspi.2012.11.003.
Texto completo da fonteLewin, Peter. "Rothbard and Mises on Interest: An Exercise in Theoretical Purity". Journal of the History of Economic Thought 19, n.º 1 (1997): 141–59. http://dx.doi.org/10.1017/s1053837200004727.
Texto completo da fonteAndreella, Angela, e Livio Finos. "Procrustes Analysis for High-Dimensional Data". Psychometrika, 18 de maio de 2022. http://dx.doi.org/10.1007/s11336-022-09859-5.
Texto completo da fonteNakhaei Rad, Najmeh, Andriette Bekker, Mohammad Arashi e Christophe Ley. "Coming Together of Bayesian Inference and Skew Spherical Data". Frontiers in Big Data 4 (8 de fevereiro de 2022). http://dx.doi.org/10.3389/fdata.2021.769726.
Texto completo da fonteTeses / dissertações sobre o assunto "Von Mises-Fisher prior"
Hornik, Kurt, e 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.
Texto completo da fonteTraullé, Benjamin. "Techniques d’échantillonnage pour la déconvolution aveugle bayésienne". Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0004.
Texto completo da fonteThese 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
Trabalhos de conferências sobre o assunto "Von Mises-Fisher prior"
Traulle, Benjamin, Stephanie Bidon e 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.
Texto completo da fonteЧерняев, Сергей, Sergey Chernyaev, Олег Лукашенко e 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.
Texto completo da fonteJin, Yujie, Xu Chu, Yasha Wang e 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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