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Статті в журналах з теми "A priori de von Mises-Fisher":
Hornik, Kurt, and Bettina Grün. "On conjugate families and Jeffreys priors for von Mises–Fisher distributions." Journal of Statistical Planning and Inference 143, no. 5 (May 2013): 992–99. http://dx.doi.org/10.1016/j.jspi.2012.11.003.
Ma, He, and Weipeng Wu. "A deep clustering framework integrating pairwise constraints and a VMF mixture model." Electronic Research Archive 32, no. 6 (2024): 3952–72. http://dx.doi.org/10.3934/era.2024177.
Lewin, Peter. "Rothbard and Mises on Interest: An Exercise in Theoretical Purity." Journal of the History of Economic Thought 19, no. 1 (1997): 141–59. http://dx.doi.org/10.1017/s1053837200004727.
Barrotta, Pierluigi. "A Neo-Kantian Critique of Von Mises's Epistemology." Economics and Philosophy 12, no. 1 (April 1996): 51–66. http://dx.doi.org/10.1017/s0266267100003710.
Scheall, Scott. "HAYEK THE APRIORIST?" Journal of the History of Economic Thought 37, no. 1 (February 12, 2015): 87–110. http://dx.doi.org/10.1017/s1053837214000765.
Michel, Nicolas, Giovanni Chierchia, Romain Negrel, and 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, no. 13 (March 24, 2024): 14350–58. http://dx.doi.org/10.1609/aaai.v38i13.29348.
Chang-Chien, Shou-Jen, Wajid Ali, and Miin-Shen Yang. "A Learning-Based EM Clustering for Circular Data with Unknown Number of Clusters." Proceedings of Engineering and Technology Innovation 15 (April 27, 2020): 42–51. http://dx.doi.org/10.46604/peti.2020.5241.
V. Le, Canh, Phuc L. H. Ho, and Hoa T. Nguyen. "Airy-based equilibrium mesh-free method for static limit analysis of plane problems." Vietnam Journal of Mechanics 38, no. 3 (September 25, 2016): 167–79. http://dx.doi.org/10.15625/0866-7136/38/3/5961.
Robitaille, Christian. "La question de la connaissance a priori en sciences sociales : les points de vue de Simiand, Mises et Simmel." Revue de philosophie économique Vol. 24, no. 2 (December 22, 2023): 63–91. http://dx.doi.org/10.3917/rpec.242.0063.
Strzalka, Carsten, and Manfred Zehn. "The Influence of Loading Position in A Priori High Stress Detection using Mode Superposition." Reports in Mechanical Engineering 1, no. 1 (October 24, 2020): 93–102. http://dx.doi.org/10.31181/rme200101093s.
Дисертації з теми "A priori de von Mises-Fisher":
Hornik, Kurt, and 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.
Traullé, Benjamin. "Techniques d’échantillonnage pour la déconvolution aveugle bayésienne." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0004.
These 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
Mismer, Romain. "Convergence et spike and Slab Bayesian posterior distributions in some high dimensional models." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC064.
The first main focus is the sparse Gaussian sequence model. An Empirical Bayes approach is used on the Spike and Slab prior to derive minimax convergence of the posterior second moment for Cauchy Slabs and a suboptimality result for the Laplace Slab is proved. Next, with a special choice of Slab convergence with the sharp minimax constant is derived. The second main focus is the density estimation model using a special Polya tree prior where the variables in the tree construction follow a Spike and Slab type distribution. Adaptive minimax convergence in the supremum norm of the posterior distribution as well as a nonparametric Bernstein-von Mises theorem are obtained
Parr, Bouberima Wafia. "Modèles de mélange de von Mises-Fisher." Phd thesis, Université René Descartes - Paris V, 2013. http://tel.archives-ouvertes.fr/tel-00987196.
Parr, Bouberima Wafia. "Modèles de mélange de von Mises-Fisher." Electronic Thesis or Diss., Paris 5, 2013. http://www.theses.fr/2013PA05S028.
In contemporary life directional data are present in most areas, in several forms, aspects and large sizes / dimensions; hence the need for effective methods of studying the existing problems in these fields. To solve the problem of clustering, the probabilistic approach has become a classic approach, based on the simple idea: since the g classes are different from each other, it is assumed that each class follows a distribution of probability, whose parameters are generally different from one class to another. We are concerned here with mixture modelling. Under this assumption, the initial data are considered as a sample of a d-dimensional random variable whose density is a mixture of g distributions of probability where each one is specific to a class. In this thesis we are interested in the clustering of directional data that has been treated using known classification methods which are the most appropriate for this case. In which both approaches the geometric and the probabilistic one have been considered. In the first, some kmeans like algorithms have been explored and considered. In the second, by directly handling the estimation of parameters from which is deduced the partition maximizing the log-likelihood, this approach is represented by the EM algorithm. For the latter approach, model mixtures of distributions of von Mises-Fisher have been used, proposing variants of the EM algorithm: EMvMF, the CEMvMF, the SEMvMF and the SAEMvMF. In the same context, the problem of finding the number of the components in the mixture and the choice of the model, using some information criteria {Bic, Aic, Aic3, Aic4, AICC, AICU, CAIC, Clc, Icl-Bic, LI, Icl, Awe} have been discussed. The study concludes with a comparison of the used vMF model with a simpler exponential model. In the latter, it is assumed that all data are distributed on a hypersphere of a predetermined radius greater than one, instead of a unit hypersphere in the case of the vMF model. An improvement of this method based on the estimation step of the radius in the algorithm NEMρ has been proposed: this allowed us in most of our applications to find the best partitions; we have developed also the NCEMρ and NSEMρ algorithms. The algorithms proposed in this work were performed on a variety of textual data, genetic data and simulated data according to the vMF model; these applications gave us a better understanding of the different studied approaches throughout this thesis
Launay, Tristan. "Méthodes bayésiennes pour la prévision de consommation l'électricité." Phd thesis, Université de Nantes, 2012. http://tel.archives-ouvertes.fr/tel-00766237.
Abeywardana, Sachinthaka. "Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/16504.
Hornik, Kurt, and Bettina Grün. "movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions." American Statistical Association, 2014. http://epub.wu.ac.at/4893/1/v58i10.pdf.
Hornik, Kurt, and Bettina Grün. "On Maximum Likelihood Estimation of the Concentration Parameter of von Mises-Fisher Distributions." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3669/1/Report120.pdf.
Series: Research Report Series / Department of Statistics and Mathematics
Salah, Aghiles. "Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB093/document.
Cluster analysis or clustering, which aims to group together similar objects, is undoubtedly a very powerful unsupervised learning technique. With the growing amount of available data, clustering is increasingly gaining in importance in various areas of data science for several reasons such as automatic summarization, dimensionality reduction, visualization, outlier detection, speed up research engines, organization of huge data sets, etc. Existing clustering approaches are, however, severely challenged by the high dimensionality and extreme sparsity of the data sets arising in some current areas of interest, such as Collaborative Filtering (CF) and text mining. Such data often consists of thousands of features and more than 95% of zero entries. In addition to being high dimensional and sparse, the data sets encountered in the aforementioned domains are also directional in nature. In fact, several previous studies have empirically demonstrated that directional measures—that measure the distance between objects relative to the angle between them—, such as the cosine similarity, are substantially superior to other measures such as Euclidean distortions, for clustering text documents or assessing the similarities between users/items in CF. This suggests that in such context only the direction of a data vector (e.g., text document) is relevant, not its magnitude. It is worth noting that the cosine similarity is exactly the scalar product between unit length data vectors, i.e., L 2 normalized vectors. Thus, from a probabilistic perspective using the cosine similarity is equivalent to assuming that the data are directional data distributed on the surface of a unit-hypersphere. Despite the substantial empirical evidence that certain high dimensional sparse data sets, such as those encountered in the above domains, are better modeled as directional data, most existing models in text mining and CF are based on popular assumptions such as Gaussian, Multinomial or Bernoulli which are inadequate for L 2 normalized data. In this thesis, we focus on the two challenging tasks of text document clustering and item recommendation, which are still attracting a lot of attention in the domains of text mining and CF, respectively. In order to address the above limitations, we propose a suite of new models and algorithms which rely on the von Mises-Fisher (vMF) assumption that arises naturally for directional data lying on a unit-hypersphere
Частини книг з теми "A priori de von Mises-Fisher":
Gatto, Riccardo. "The Generalized von Mises℃Fisher Distribution." In Advances in Directional and Linear Statistics, 51–68. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2628-9_4.
Salah, Aghiles, and Mohamed Nadif. "Model-based von Mises-Fisher Co-clustering with a Conscience." In Proceedings of the 2017 SIAM International Conference on Data Mining, 246–54. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974973.28.
Kumar, Ritwik, Baba C. Vemuri, Fei Wang, Tanveer Syeda-Mahmood, Paul R. Carney, and Thomas H. Mareci. "Multi-fiber Reconstruction from DW-MRI Using a Continuous Mixture of Hyperspherical von Mises-Fisher Distributions." In Lecture Notes in Computer Science, 139–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02498-6_12.
McGraw, Tim, Baba Vemuri, Robert Yezierski, and Thomas Mareci. "Segmentation of High Angular Resolution Diffusion MRI Modeled as a Field of von Mises-Fisher Mixtures." In Computer Vision – ECCV 2006, 463–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_36.
Van Linh, Ngo, Nguyen Kim Anh, Khoat Than, and Nguyen Nguyen Tat. "Effective and Interpretable Document Classification Using Distinctly Labeled Dirichlet Process Mixture Models of von Mises-Fisher Distributions." In Database Systems for Advanced Applications, 139–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18123-3_9.
Kim, Peter T. "On the Characteristic Function of the Matrix von Mises—Fisher Distribution with Application to SO(N)—Deconvolution." In High Dimensional Probability II, 477–92. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1358-1_31.
Kunze, Karsten, and Helmut Schaeben. "Ideal Patterns of Crystallographic Preferred Orientation and Their Representation by the von Mises - Fisher Matrix or Bingham Quaternion Distribution." In Materials Science Forum, 295–300. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-975-x.295.
Sra, Suvrit, Arindam Banerjee, Joydeep Ghosh, and Inderjit Dhillon. "Text Clustering with Mixture of von Mises-Fisher Distributions." In Text Mining, 121–61. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420059458.ch6.
"Text Clustering with Mixture of von Mises-Fisher Distribu- tions." In Text Mining, 151–84. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420059458-14.
Тези доповідей конференцій з теми "A priori de von Mises-Fisher":
Traulle, Benjamin, Stephanie Bidon, and 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.
Черняев, Сергей, Sergey Chernyaev, Олег Лукашенко, and 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.
Jin, Yujie, Xu Chu, Yasha Wang, and 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.
Barbaro, Florian, and Fabrice Rossi. "Sparse mixture of von Mises-Fisher distribution." In ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2021. http://dx.doi.org/10.14428/esann/2021.es2021-115.
Mash'al, Mohamadreza, and Reshad Hosseini. "K-means++ for mixtures of von Mises-Fisher Distributions." In 2015 7th Conference on Information and Knowledge Technology (IKT). IEEE, 2015. http://dx.doi.org/10.1109/ikt.2015.7288786.
Kobayashi, Takumi, and Nobuyuki Otsu. "Von Mises-Fisher Mean Shift for Clustering on a Hypersphere." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.522.
Li, Kailai, Florian Pfaff, and Uwe D. Hanebeck. "Nonlinear von Mises–Fisher Filtering Based on Isotropic Deterministic Sampling." In 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2020. http://dx.doi.org/10.1109/mfi49285.2020.9235260.
Ramachandran, Vinod, and Tahir Ahmad. "Modeling of epileptic seizures using the von Mises-Fisher distribution." In PROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Mathematical Sciences Exploration for the Universal Preservation. Author(s), 2017. http://dx.doi.org/10.1063/1.4995890.
Skabar, Andrew, and Shahmeer Memon. "Density estimation-based document categorization using von Mises-Fisher kernels." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596595.
Traa, Johannes, and Paris Smaragdis. "Multiple speaker tracking with the Factorial von Mises-Fisher Filter." In 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958891.