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Автор: Grafiati

Опубліковано: 7 липня 2024

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1

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.

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2

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.

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<abstract><p>We presented a novel deep generative clustering model called Variational Deep Embedding based on Pairwise constraints and the Von Mises-Fisher mixture model (VDEPV). VDEPV consists of fully connected neural networks capable of learning latent representations from raw data and accurately predicting cluster assignments. Under the assumption of a genuinely non-informative prior, VDEPV adopted a von Mises-Fisher mixture model to depict the hyperspherical interpretation of the data. We defined and established pairwise constraints by employing a random sample mining strategy and applying data augmentation techniques. These constraints enhanced the compactness of intra-cluster samples in the spherical embedding space while improving inter-cluster samples' separability. By minimizing Kullback-Leibler divergence, we formulated a clustering loss function based on pairwise constraints, which regularized the joint probability distribution of latent variables and cluster labels. Comparative experiments with other deep clustering methods demonstrated the excellent performance of VDEPV.</p></abstract>

3

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.

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The conventional wisdom in economics holds, with Irving Fisher, that interest is explained jointly by the forces of time preference (thrift) and productivity. One school of thought, however, has held stubbornly to the assertion that interest is best understood as a result of time preference alone, time preference as the essential determinant of interest. This is the pure time preference approach to interest. And while most economists are inclined to dismiss this approach out of hand, the pure time preference approach has proved remarkably resilient. Part of the explanation for the persistence of rival theories can be found, not surprisingly, in terminological confusions and ambiguities, for example in deciding among candidates for essential causation. I hope in this article to improve the case for the pure time preference approach to interest by clarifying the argument. It appears that some of the confusion can be attributed to the approach of two theorists, Ludwig von Mises and Murray Rothbard, and to their connecting the time preference approach to their particular a priori methodology.

4

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.

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More than many other Austrians, Mises tried to found aprioristic methodology on a well defined and developed epistemology. Although references to Kant are scattered rather unsystematically throughout his works, he nevertheless used an unequivocal Kantian terminology. He explicitly defended the existence of ‘a priori knowledge’, ‘synthetic a priori propositions’, ‘the category of action’, and so forth.

5

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.

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The paper argues that Terence Hutchison’s (1981) argument that the young F. A. Hayek maintained a methodological position markedly similar to that of Ludwig von Mises fails to support the relevant conclusion. The first problem with Hutchison’s argument is that it is not clear exactly what conclusion he meant to establish. Mises (in)famously maintained a rather extreme methodological apriorism. However, the concept of a priori knowledge that emerges from Hayek’s epistemology as implied in his work on theoretical psychology is the opposite of Mises’s treatment of a priori knowledge. Thus, it cannot be maintained—if, indeed, Hutchison meant to establish—that Hayek was a Misesian apriorist during the years in question. What’s more, the paper shows that Hutchison’s argument does not support a weaker interpretation of the relevant conclusion. There are alternative interpretations of Hutchison’s evidence, more charitable and more consistent with Hayek’s epistemology, which undermine Hutchison’s conclusion.

6

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.

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We use the maximum a posteriori estimation principle for learning representations distributed on the unit sphere. We propose to use the angular Gaussian distribution, which corresponds to a Gaussian projected on the unit-sphere and derive the associated loss function. We also consider the von Mises-Fisher distribution, which is the conditional of a Gaussian in the unit-sphere. The learned representations are pushed toward fixed directions, which are the prior means of the Gaussians; allowing for a learning strategy that is resilient to data drift. This makes it suitable for online continual learning, which is the problem of training neural networks on a continuous data stream, where multiple classification tasks are presented sequentially so that data from past tasks are no longer accessible, and data from the current task can be seen only once. To address this challenging scenario, we propose a memory-based representation learning technique equipped with our new loss functions. Our approach does not require negative data or knowledge of task boundaries and performs well with smaller batch sizes while being computationally efficient. We demonstrate with extensive experiments that the proposed method outperforms the current state-of-the-art methods on both standard evaluation scenarios and realistic scenarios with blurry task boundaries. For reproducibility, we use the same training pipeline for every compared method and share the code at https://github.com/Nicolas1203/ocl-fd.

7

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.

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Clustering is a method for analyzing grouped data. Circular data were well used in various applications, such as wind directions, departure directions of migrating birds or animals, etc. The expectation & maximization (EM) algorithm on mixtures of von Mises distributions is popularly used for clustering circular data. In general, the EM algorithm is sensitive to initials and not robust to outliers in which it is also necessary to give a number of clusters a priori. In this paper, we consider a learning-based schema for EM, and then propose a learning-based EM algorithm on mixtures of von Mises distributions for clustering grouped circular data. The proposed clustering method is without any initial and robust to outliers with automatically finding the number of clusters. Some numerical and real data sets are used to compare the proposed algorithm with existing methods. Experimental results and comparisons actually demonstrate these good aspects of effectiveness and superiority of the proposed learning-based EM algorithm.

8

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.

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This paper presents a numerical procedure for lower bound limit analysis of plane problems governed by von Mises yield criterion. The stress fields are calculated based on the Airy function which is approximated using the moving least squares technique. With the use of the Airy-based equilibrium mesh-free method, equilibrium equations are ensured to be automatically satisfied a priori, and the size of the resulting optimization problem is reduced significantly. Various plane strain and plane stress with arbitrary geometries and boundary conditions are examined to illustrate the performance of the proposed procedure.

9

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.

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Les sciences sociales contemporaines se caractérisent par un abandon de la quête d’une véritable connaissance a priori non-relativiste. D’une part, les méthodes quantitatives et le positivisme méthodologique rejettent en général la possibilité de l’acquisition de ce type de savoir. D’autre part, les méthodes qualitatives et les approches herméneutiques, lorsqu’elles ne cherchent pas explicitement à obtenir des connaissances a posteriori , se caractérisent généralement par un apriorisme sceptique selon lequel l’adoption de n’importe quelle perspective ou cadre théorique est considérée valable. Cet article propose d’évaluer trois perspectives différentes sur la possibilité de la connaissance a priori en sciences sociales, c’est-à-dire celles de François Simiand (critique de l’apriorisme), Ludwig von Mises (partisan de l’apriorisme praxéologique) et de Georg Simmel (initiateur d’un apriorisme formaliste). Cette évaluation comparative permet de mettre en évidence la portée et les limites de l’apriorisme en ce qui a trait à l’acquisition de connaissances en sciences sociales. Elle permet, en dernière analyse, de rendre à l’apriorisme ses lettres de noblesse et d’ainsi faciliter son éventuel retour sous une forme qui échapperait au relativisme actuel.

10

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.

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For the analysis of structural components, the finite element method (FEM) has become the most widely applied tool for numerical stress- and subsequent durability analyses. In industrial application advanced FE-models result in high numbers of degrees of freedom, making dynamic analyses time-consuming and expensive. As detailed finite element models are necessary for accurate stress results, the resulting data and connected numerical effort from dynamic stress analysis can be high. For the reduction of that effort, sophisticated methods have been developed to limit numerical calculations and processing of data to only small fractions of the global model. Therefore, detailed knowledge of the position of a component’s highly stressed areas is of great advantage for any present or subsequent analysis steps. In this paper an efficient method for the a priori detection of highly stressed areas of force-excited components is presented, based on modal stress superposition. As the component’s dynamic response and corresponding stress is always a function of its excitation, special attention is paid to the influence of the loading position. Based on the frequency domain solution of the modally decoupled equations of motion, a coefficient for a priori weighted superposition of modal von Mises stress fields is developed and validated on a simply supported cantilever beam structure with variable loading positions. The proposed approach is then applied to a simplified industrial model of a twist beam rear axle.

11

El mokhtari, Karim, Serge Reboul, Georges Stienne, Jean Bernard Choquel, Benaissa Amami, and Mohammed Benjelloun. "An IMM Filter Defined in the Linear-Circular Domain, Application to Maneuver Detection with Heading Only." Mathematical Problems in Engineering 2018 (November 6, 2018): 1–14. http://dx.doi.org/10.1155/2018/3531075.

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In this article, we propose a multimodel filter for circular data. The so-called Circular Interacting Multimodel filter is derived in a Bayesian framework with the circular normal von Mises distribution. The aim of the proposed filter is to obtain the same performance in the circular domain as the classical IMM filter in the linear domain. In our approach, the mixing and fusion stages of the Circular Interacting Multimodel filter are, respectively, defined from the a priori and from the a posteriori circular distributions of the state angle knowing the measurements and according to a set of models. We propose in this article a set of circular models that will be used in order to detect the vehicle maneuvers from heading measurements. The Circular Interacting Multimodel filter performances are assessed on synthetic data and we show on real data a vehicle maneuver detection application.

12

Cao, Mingxuan, Kai Xie, Feng Liu, Bohao Li, Chang Wen, Jianbiao He, and Wei Zhang. "Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network." Sensors 23, no. 6 (March 22, 2023): 3355. http://dx.doi.org/10.3390/s23063355.

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Aiming at the recognition of intelligent retail dynamic visual container goods, two problems that lead to low recognition accuracy must be addressed; one is the lack of goods features caused by the occlusion of the hand, and the other is the high similarity of goods. Therefore, this study proposes an approach for occluding goods recognition based on a generative adversarial network combined with prior inference to address the two abovementioned problems. With DarkNet53 as the backbone network, semantic segmentation is used to locate the occluded part in the feature extraction network, and simultaneously, the YOLOX decoupling head is used to obtain the detection frame. Subsequently, a generative adversarial network under prior inference is used to restore and expand the features of the occluded parts, and a multi-scale spatial attention and effective channel attention weighted attention mechanism module is proposed to select fine-grained features of goods. Finally, a metric learning method based on von Mises–Fisher distribution is proposed to increase the class spacing of features to achieve the effect of feature distinction, whilst the distinguished features are utilized to recognize goods at a fine-grained level. The experimental data used in this study were all obtained from the self-made smart retail container dataset, which contains a total of 12 types of goods used for recognition and includes four couples of similar goods. Experimental results reveal that the peak signal-to-noise ratio and structural similarity under improved prior inference are 0.7743 and 0.0183 higher than those of the other models, respectively. Compared with other optimal models, mAP improves the recognition accuracy by 1.2% and the recognition accuracy by 2.82%. This study solves two problems: one is the occlusion caused by hands, and the other is the high similarity of goods, thus meeting the requirements of commodity recognition accuracy in the field of intelligent retail and exhibiting good application prospects.

13

Fang, Jinyuan, Shangsong Liang, Zaiqiao Meng, and Maarten De Rijke. "Hyperspherical Variational Co-embedding for Attributed Networks." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–36. http://dx.doi.org/10.1145/3478284.

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Network-based information has been widely explored and exploited in the information retrieval literature. Attributed networks, consisting of nodes, edges as well as attributes describing properties of nodes, are a basic type of network-based data, and are especially useful for many applications. Examples include user profiling in social networks and item recommendation in user-item purchase networks. Learning useful and expressive representations of entities in attributed networks can provide more effective building blocks to down-stream network-based tasks such as link prediction and attribute inference. Practically, input features of attributed networks are normalized as unit directional vectors. However, most network embedding techniques ignore the spherical nature of inputs and focus on learning representations in a Gaussian or Euclidean space, which, we hypothesize, might lead to less effective representations. To obtain more effective representations of attributed networks, we investigate the problem of mapping an attributed network with unit normalized directional features into a non-Gaussian and non-Euclidean space. Specifically, we propose a hyperspherical variational co-embedding for attributed networks (HCAN), which is based on generalized variational auto-encoders for heterogeneous data with multiple types of entities. HCAN jointly learns latent embeddings for both nodes and attributes in a unified hyperspherical space such that the affinities between nodes and attributes can be captured effectively. We argue that this is a crucial feature in many real-world applications of attributed networks. Previous Gaussian network embedding algorithms break the assumption of uninformative prior, which leads to unstable results and poor performance. In contrast, HCAN embeds nodes and attributes as von Mises-Fisher distributions, and allows one to capture the uncertainty of the inferred representations. Experimental results on eight datasets show that HCAN yields better performance in a number of applications compared with nine state-of-the-art baselines.

14

Kurz, Gerhard, Igor Gilitschenski, and Uwe D. Hanebeck. "Unscented von Mises–Fisher Filtering." IEEE Signal Processing Letters 23, no. 4 (April 2016): 463–67. http://dx.doi.org/10.1109/lsp.2016.2529854.

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15

Figueiredo, Adelaide. "Clustering Directions Based on the Estimation of a Mixture of Von Mises-Fisher Distributions." Open Statistics & Probability Journal 08, no. 1 (December 29, 2017): 39–52. http://dx.doi.org/10.2174/1876527001708010039.

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Background:In the statistical analysis of directional data, the von Mises-Fisher distribution plays an important role to model unit vectors. The estimation of the parameters of a mixture of von Mises-Fisher distributions can be done through the Estimation-Maximization algorithm.Objective:In this paper we propose a dynamic clusters type algorithm based on the estimation of the parameters of a mixture of von Mises-Fisher distributions for clustering directions, and we compare this algorithm with the Estimation-Maximization algorithm. We also define the between-groups and within-groups variability measures to compare the solutions obtained with the algorithms through these measures.Results:The comparison of the clusters obtained with both algorithms is provided for a simulation study based on samples generated from a mixture of two Fisher distributions and for an illustrative example with spherical data.

16

Wood, Andrew T. A. "Simulation of the von mises fisher distribution." Communications in Statistics - Simulation and Computation 23, no. 1 (January 1994): 157–64. http://dx.doi.org/10.1080/03610919408813161.

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17

Tanabe, Akihiro, Kenji Fukumizu, Shigeyuki Oba, Takashi Takenouchi, and Shin Ishii. "Parameter estimation for von Mises–Fisher distributions." Computational Statistics 22, no. 1 (March 7, 2007): 145–57. http://dx.doi.org/10.1007/s00180-007-0030-7.

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18

Watanabe, Kazuho, Hsiang-Yun Wu, Shigeo Takahashi, and Issei Fujishiro. "Asymmetric biclustering with constrained von Mises-Fisher models." Journal of Physics: Conference Series 699 (March 2016): 012018. http://dx.doi.org/10.1088/1742-6596/699/1/012018.

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19

GHODSI, MARYAM. "NONSTANDARD ESTIMATION FOR THE VON MISES FISHER DISTRIBUTION." Bulletin of the Australian Mathematical Society 97, no. 3 (March 7, 2018): 520–22. http://dx.doi.org/10.1017/s0004972717001228.

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20

Rivest, Louis-Paul. "Spherical Regression for Concentrated Fisher-Von Mises Distributions." Annals of Statistics 17, no. 1 (March 1989): 307–17. http://dx.doi.org/10.1214/aos/1176347018.

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21

Figueiredo, Adelaide. "Discriminant Analysis for the von Mises-Fisher Distribution." Communications in Statistics - Simulation and Computation 38, no. 9 (October 2009): 1991–2003. http://dx.doi.org/10.1080/03610910903200281.

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22

Hillen, Thomas, Kevin J. Painter, Amanda C. Swan, and Albert D. Murtha. "Moments of von mises and fisher distributions and applications." Mathematical Biosciences and Engineering 14, no. 3 (2017): 673–94. http://dx.doi.org/10.3934/mbe.2017038.

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23

Bagchi, Parthasarathy, and Irwin Guttman. "Theoretical considerations of the multivariate von Mises-Fisher distribution." Journal of Applied Statistics 15, no. 2 (January 1988): 149–69. http://dx.doi.org/10.1080/02664768800000022.

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24

Romanazzi, Mario. "Discriminant Analysis with High Dimensional von Mises - Fisher Distributions." ATHENS JOURNAL OF SCIENCES 1, no. 4 (November 30, 2014): 225–40. http://dx.doi.org/10.30958/ajs.1-4-1.

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25

Song, Heping, Jun Liu, and Guoli Wang. "High-order parameter approximation for von Mises–Fisher distributions." Applied Mathematics and Computation 218, no. 24 (August 2012): 11880–90. http://dx.doi.org/10.1016/j.amc.2012.05.050.

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26

Li, Kailai, Florian Pfaff, and Uwe D. Hanebeck. "Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation." Sensors 21, no. 9 (April 24, 2021): 2991. http://dx.doi.org/10.3390/s21092991.

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In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises–Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises–Fisher filters and the particle filter.

27

Christie, David. "Efficient von Mises–Fisher concentration parameter estimation using Taylor series." Journal of Statistical Computation and Simulation 85, no. 16 (October 7, 2014): 3259–65. http://dx.doi.org/10.1080/00949655.2014.965169.

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28

Salah, Aghiles, and Mohamed Nadif. "Social regularized von Mises–Fisher mixture model for item recommendation." Data Mining and Knowledge Discovery 31, no. 5 (March 24, 2017): 1218–41. http://dx.doi.org/10.1007/s10618-017-0499-9.

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29

Larsen, P. V. "Improved likelihood ratio tests on the von Mises-Fisher distribution." Biometrika 89, no. 4 (December 1, 2002): 947–51. http://dx.doi.org/10.1093/biomet/89.4.947.

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30

Figueiredo, Adelaide Maria Sousa. "Goodness-of-fit for a concentrated von Mises-Fisher distribution." Computational Statistics 27, no. 1 (February 16, 2011): 69–82. http://dx.doi.org/10.1007/s00180-011-0238-4.

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31

Baricz, Árpád. "Remarks on a parameter estimation for von Mises–Fisher distributions." Computational Statistics 29, no. 3-4 (April 22, 2014): 891–94. http://dx.doi.org/10.1007/s00180-014-0493-2.

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32

Anisse, Khald, Radgui Amina, and Rziza Mohamed. "Spherical Object Tracking using Von Mises-Fisher Distribution in Catadioptric System." Journal of Computer Science 16, no. 9 (September 1, 2020): 1229–36. http://dx.doi.org/10.3844/jcssp.2020.1229.1236.

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33

Mammasis, Konstantinos, Robert W. Stewart, and John S. Thompson. "Spatial Fading Correlation model using mixtures of Von Mises Fisher distributions." IEEE Transactions on Wireless Communications 8, no. 4 (April 2009): 2046–55. http://dx.doi.org/10.1109/twc.2009.080505.

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34

Garcia-Fernandez, Angel F., Filip Tronarp, and Simo Sarkka. "Gaussian Target Tracking With Direction-of-Arrival von Mises–Fisher Measurements." IEEE Transactions on Signal Processing 67, no. 11 (June 1, 2019): 2960–72. http://dx.doi.org/10.1109/tsp.2019.2911258.

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35

Anderson, Craig A., Paul D. Teal, and Mark A. Poletti. "Spatially Robust Far-field Beamforming Using the von Mises(-Fisher) Distribution." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 12 (December 2015): 2189–97. http://dx.doi.org/10.1109/taslp.2015.2473684.

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36

Røge, Rasmus E., Kristoffer H. Madsen, Mikkel N. Schmidt, and Morten Mørup. "Infinite von Mises–Fisher Mixture Modeling of Whole Brain fMRI Data." Neural Computation 29, no. 10 (October 2017): 2712–41. http://dx.doi.org/10.1162/neco_a_01000.

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Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises–Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data.

37

Schaeben, H. "A Note on a Generalized Standard Orientation Distribution in PDF-Component Fit Methods." Textures and Microstructures 23, no. 1 (January 1, 1995): 1–5. http://dx.doi.org/10.1155/tsm.23.1.

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A generalized model orientation distribution which was recently introduced into texture analysis is identified as von Mises-Fisher matrix distribution on SO(3) or, equivalently, as Bingham distribution on S+4⊂IR4. The one-one correspondence of the distributions is explicitly given.

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Ferreira da Silva, Adelino R. "Computational Representation of White Matter Fiber Orientations." International Journal of Biomedical Imaging 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/232143.

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We present a new methodology based on directional data clustering to represent white matter fiber orientations in magnetic resonance analyses for high angular resolution diffusion imaging. A probabilistic methodology is proposed for estimating intravoxel principal fiber directions, based on clustering directional data arising from orientation distribution function (ODF) profiles. ODF reconstructions are used to estimate intravoxel fiber directions using mixtures of von Mises-Fisher distributions. The method focuses on clustering data on the unit sphere, where complexity arises from representing ODF profiles as directional data. The proposed method is validated on synthetic simulations, as well as on a real data experiment. Based on experiments, we show that by clustering profile data using mixtures of von Mises-Fisher distributions it is possible to estimate multiple fiber configurations in a more robust manner than currently used approaches, without recourse to regularization or sharpening procedures. The method holds promise to support robust tractographic methodologies and to build realistic models of white matter tracts in the human brain.

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Castillo, Ismaël. "A semiparametric Bernstein–von Mises theorem for Gaussian process priors." Probability Theory and Related Fields 152, no. 1-2 (August 11, 2010): 53–99. http://dx.doi.org/10.1007/s00440-010-0316-5.

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40

Chan, Y. M., and Xuming He. "On Median-Type Estimators of Direction for the von Mises-Fisher Distribution." Biometrika 80, no. 4 (December 1993): 869. http://dx.doi.org/10.2307/2336878.

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41

Guan, Hao, and William A. P. Smith. "Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution." IEEE Transactions on Image Processing 26, no. 2 (February 2017): 711–23. http://dx.doi.org/10.1109/tip.2016.2621662.

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42

Taghia, Jalil, Zhanyu Ma, and Arne Leijon. "Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 9 (September 2014): 1701–15. http://dx.doi.org/10.1109/tpami.2014.2306426.

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43

Ma, Zhanyu, Jalil Taghia, W. Bastiaan Kleijn, Arne Leijon, and Jun Guo. "Line spectral frequencies modeling by a mixture of von Mises–Fisher distributions." Signal Processing 114 (September 2015): 219–24. http://dx.doi.org/10.1016/j.sigpro.2015.02.015.

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44

Jie, Zhou, Cao Zhigang, and Hisakazu Kikuchi. "Analysis of MIMO antenna array based on 3D Von Mises Fisher distribution." Journal of China Universities of Posts and Telecommunications 22, no. 2 (April 2015): 15–23. http://dx.doi.org/10.1016/s1005-8885(15)60634-3.

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45

Watamori, Yoko, and Teruo Fujioka. "Confidence Regions for the Mean Direction of the Von Mises–Fisher Distribution." Communications in Statistics - Theory and Methods 34, no. 3 (March 2005): 671–78. http://dx.doi.org/10.1081/sta-200052132.

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46

CHAN, Y. M., and XUMING HE. "On median-type estimators of direction for the von Mises—Fisher distribution." Biometrika 80, no. 4 (1993): 869–75. http://dx.doi.org/10.1093/biomet/80.4.869.

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47

Ko, Daijin. "Robust Estimation of the Concentration Parameter of the Von Mises-Fisher Distribution." Annals of Statistics 20, no. 2 (June 1992): 917–28. http://dx.doi.org/10.1214/aos/1176348663.

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48

Nuñez-antonio, G., and E. Gutiérrez-peña. "A Bayesian Analysis of Directional Data Using the von Mises–Fisher Distribution." Communications in Statistics - Simulation and Computation 34, no. 4 (October 2005): 989–99. http://dx.doi.org/10.1080/03610910500308495.

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49

Scealy, J. L., and Andrew T. A. Wood. "Scaled von Mises–Fisher Distributions and Regression Models for Paleomagnetic Directional Data." Journal of the American Statistical Association 114, no. 528 (April 30, 2019): 1547–60. http://dx.doi.org/10.1080/01621459.2019.1585249.

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50

Lopez-Moreno, I., D. Ramos, J. Gonzalez-Dominguez, and J. Gonzalez-Rodriguez. "Von Mises–Fisher Models in the Total Variability Subspace for Language Recognition." IEEE Signal Processing Letters 18, no. 12 (December 2011): 705–8. http://dx.doi.org/10.1109/lsp.2011.2170566.

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