Academic literature on the topic 'Matrice de Fisher'
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Journal articles on the topic "Matrice de Fisher"
Sanon, Abdramane, Alain P. K. Gomgnimbou, Hamadé Sigue, Kalifa Coulibaly, Cheick A. Bambara, Willifried Sanou, Sékou Fofana, and Hassan B. Nacro. "Performances économiques et financières de la fertilisation en riziculture pluviale stricte dans la zone sud soudanienne du Burkina Faso." International Journal of Biological and Chemical Sciences 15, no. 4 (November 19, 2021): 1581–94. http://dx.doi.org/10.4314/ijbcs.v15i4.22.
Full textHeavens, A. F., M. Seikel, B. D. Nord, M. Aich, Y. Bouffanais, B. A. Bassett, and M. P. Hobson. "Generalized Fisher matrices." Monthly Notices of the Royal Astronomical Society 445, no. 2 (October 14, 2014): 1687–93. http://dx.doi.org/10.1093/mnras/stu1866.
Full textHeavens, Alan. "Generalisations of Fisher Matrices." Entropy 18, no. 6 (June 22, 2016): 236. http://dx.doi.org/10.3390/e18060236.
Full textAlmestady, Mohammed S., and Alun O. Morris. "Fischer Matrices for Projective Representations of Generalized Symmetric Groups." Algebra Colloquium 16, no. 03 (September 2009): 449–62. http://dx.doi.org/10.1142/s1005386709000431.
Full textPrins, Abraham Love. "On the Fischer matrices of a group of shape 21+2n + :G." Revista Colombiana de Matemáticas 56, no. 2 (April 17, 2023): 189–211. http://dx.doi.org/10.15446/recolma.v56n2.108379.
Full textKagan, Abram, and Zinoviy Landsman. "Relation between the covariance and Fisher information matrices." Statistics & Probability Letters 42, no. 1 (March 1999): 7–13. http://dx.doi.org/10.1016/s0167-7152(98)00178-3.
Full textBöttcher, Albrecht, and Bernd Silbermann. "Toeplitz matrices and determinants with Fisher-Hartwig symbols." Journal of Functional Analysis 63, no. 2 (September 1985): 178–214. http://dx.doi.org/10.1016/0022-1236(85)90085-0.
Full textBin, Meng. "Operator-valued free Fisher information of random matrices." Acta Mathematica Scientia 30, no. 4 (July 2010): 1327–37. http://dx.doi.org/10.1016/s0252-9602(10)60128-2.
Full textBöttcher, Albrecht, and Jani Virtanen. "Norms of Toeplitz Matrices with Fisher–Hartwig Symbols." SIAM Journal on Matrix Analysis and Applications 29, no. 2 (January 2007): 660–71. http://dx.doi.org/10.1137/06066165x.
Full textLv, Songjun. "General Fisher information matrices of a random vector." Advances in Applied Mathematics 89 (August 2017): 18–40. http://dx.doi.org/10.1016/j.aam.2017.03.002.
Full textDissertations / Theses on the topic "Matrice de Fisher"
Nguyen, Thu Thuy. "Developpement de la matrice d'information de Fisher pour des modèles non linéaires à effets mixtes : application à la pharmacocinétique des antibiotiques et l'impact sur l'émergence de la résistance." Paris 7, 2013. http://www.theses.fr/2013PA077029.
Full textNonlinear mixed effect models (NLMEM) can be used to analyse longitudinal data in patients, for example in pharmacokinetic/pharmacodynamic studies, with fewer samples than the classical non-compartmental approach. A method for designing these studies is to use the Fisher information matrix (MF), approximated by first order linearization of the model. We extended this expression of MF to take into account the within subject variability and the discrete covariates. These developments were evaluated by simulations, implemented in PFIM 3. 2 dedicated to design evaluation and optimisation. We also applied PFIM to design a crossover study, showing absence of interaction of a compound on the pharmacokinetic of amoxicillin. We also proposed and evaluated by simulations an alternative approach to compute MF without linearization, based on Gaussian quadrature and stochastic integration. This approach gave more correct predictions than linearization when the model becomes very nonlinear but it is very time consuming; consequently its use is limited to NLMEM with only few random effects. Next, we studied the expansion of résistance to fluoroquinolones in intestinal flora. In a trial in piglets, we found, by non-compartmental approach, a significant correlation between fecal concentrations of ciprofloxacin and counts of resistant enterobacteria. Then we developed a mechanistic model to more precisely characterize the pharmacokinetic of fecal ciprofloxacin as well as the kinetics of susceptible and resistant enterobacteria. To our knowledge, this is the first in vivo modelling to study the bacterial résistance to fluoroquinolones in intestinal flora
Koroko, Abdoulaye. "Natural gradient-based optimization methods for deep neural networks." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG068.
Full textThe stochastic gradient method is currently the prevailing technology for training neural networks. Compared to a classical descent, the calculation of the true gradient as an average over the data is replaced by a random element of the sum. When dealing with massive data, this bold approximation enables one to decrease the number of elementary gradient evaluations and to alleviate the cost of each iteration. The price to be paid is the appearance of oscillations and the slowness of convergence, which is often excessive in terms of number of iterations. The aim of this thesis is to design an approach that is both: (i) more robust, using the fundamental methods that have been successfully proven in classical optimization, i.e., outside the learning framework; and (ii) faster in terms of convergence speed. We are especially interested in second-order methods, known for their stability and speed of convergence. To circumvent the bottleneck of these methods, which lies in the prohibitive cost of an iteration involving a linear system with a full matrix, we attempt to improve an approximation recently introduced as Kronecker-Factorized Approximation of Curvature (KFAC) for the Fisher matrix, which replaces the Hessian matrix in this context. More specifically, our work focuses on: (i) building new Kronecker factorizations based on a more rigorous mathematical justification than in KFAC; (ii) taking into account the information from the off-diagonal blocks of the Fisher matrix, which represent the interaction between the different layers; (iii) generalizing KFAC to a network architecture other than those for which it had been initially developed
Roy, Prateep Kumar. "Analysis & design of control for distributed embedded systems under communication constraints." Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00534012.
Full textRoy, Prateep Kumar. "Analyse et conception de la commande des systèmes embarqués distribués sous des contraintes de communication." Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00532883.
Full textLey, Christophe. "Univariate and multivariate symmetry: statistical inference and distributional aspects." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210029.
Full textThe first part, composed of Chapters 1, 2 and 3 of the thesis, solves two conjectures associated with multivariate skew-symmetric distributions. Since the introduction in 1985 by Adelchi Azzalini of the most famous representative of that class of distributions, namely the skew-normal distribution, it is well-known that, in the vicinity of symmetry, the Fisher information matrix is singular and the profile log-likelihood function for skewness admits a stationary point whatever the sample under consideration. Since that moment, researchers have tried to determine the subclasses of skew-symmetric distributions who suffer from each of those problems, which has led to the aforementioned two conjectures. This thesis completely solves these two problems.
The second part of the thesis, namely Chapters 4 and 5, aims at applying and constructing extremely general skewing mechanisms. As such, in Chapter 4, we make use of the univariate mechanism of Ferreira and Steel (2006) to build optimal (in the Le Cam sense) tests for univariate symmetry which are very flexible. Actually, their mechanism allowing to turn a given symmetric distribution into any asymmetric distribution, the alternatives to the null hypothesis of symmetry can take any possible shape. These univariate mechanisms, besides that surjectivity property, enjoy numerous good properties, but cannot be extended to higher dimensions in a satisfactory way. For this reason, we propose in Chapter 5 different general mechanisms, sharing all the nice properties of their competitors in Ferreira and Steel (2006), but which moreover can be extended to any dimension. We formally prove that the surjectivity property holds in dimensions k>1 and we study the principal characteristics of these new multivariate mechanisms.
Finally, the third part of this thesis, composed of Chapter 6, proposes a test for multivariate central symmetry by having recourse to the concepts of statistical depth and runs. This test extends the celebrated univariate runs test of McWilliams (1990) to higher dimensions. We analyze its asymptotic behavior (especially in dimension k=2) under the null hypothesis and its invariance and robustness properties. We conclude by an overview of possible modifications of these new tests./
Cette thèse traite de différents aspects statistiques et probabilistes de symétrie et asymétrie univariées et multivariées, et est subdivisée en trois parties distinctes.
La première partie, qui comprend les chapitres 1, 2 et 3 de la thèse, est destinée à la résolution de deux conjectures associées aux lois skew-symétriques multivariées. Depuis l'introduction en 1985 par Adelchi Azzalini du plus célèbre représentant de cette classe de lois, à savoir la loi skew-normale, il est bien connu qu'en un voisinage de la situation symétrique la matrice d'information de Fisher est singulière et la fonction de vraisemblance profile pour le paramètre d'asymétrie admet un point stationnaire quel que soit l'échantillon considéré. Dès lors, des chercheurs ont essayé de déterminer les sous-classes de lois skew-symétriques qui souffrent de chacune de ces problématiques, ce qui a mené aux deux conjectures précitées. Cette thèse résoud complètement ces deux problèmes.
La deuxième partie, constituée des chapitres 4 et 5, poursuit le but d'appliquer et de proposer des méchanismes d'asymétrisation très généraux. Ainsi, au chapitre 4, nous utilisons le méchanisme univarié de Ferreira and Steel (2006) pour construire des tests de symétrie univariée optimaux (au sens de Le Cam) qui sont très flexibles. En effet, leur méchanisme permettant de transformer une loi symétrique donnée en n'importe quelle loi asymétrique, les contre-hypothèses à la symétrie peuvent prendre toute forme imaginable. Ces méchanismes univariés, outre cette propriété de surjectivité, possèdent de nombreux autres attraits, mais ne permettent pas une extension satisfaisante aux dimensions supérieures. Pour cette raison, nous proposons au chapitre 5 des méchanismes généraux alternatifs, qui partagent toutes les propriétés de leurs compétiteurs de Ferreira and Steel (2006), mais qui en plus sont généralisables à n'importe quelle dimension. Nous démontrons formellement que la surjectivité tient en dimension k > 1 et étudions les caractéristiques principales de ces nouveaux méchanismes multivariés.
Finalement, la troisième partie de cette thèse, composée du chapitre 6, propose un test de symétrie centrale multivariée en ayant recours aux concepts de profondeur statistique et de runs. Ce test étend le célèbre test de runs univarié de McWilliams (1990) aux dimensions supérieures. Nous en analysons le comportement asymptotique (surtout en dimension k = 2) sous l'hypothèse nulle et les propriétés d'invariance et de robustesse. Nous concluons par un aperçu sur des modifications possibles de ces nouveaux tests.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Zaïdi, Abdelhamid. "Séparation aveugle d'un mélange instantané de sources autorégressives gaussiennes par la méthode du maximum de vraissemblance exact." Université Joseph Fourier (Grenoble), 2000. http://www.theses.fr/2000GRE10233.
Full textAchanta, Hema Kumari. "Optimal sensing matrices." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1421.
Full textMonaledi, R. L. "Character tables of some selected groups of extension type using Fischer-Clifford matrices." Thesis, University of the Western Cape, 2015. http://hdl.handle.net/11394/5026.
Full textThe aim of this dissertation is to calculate character tables of group extensions. There are several well developed methods for calculating the character tables of some selected group extensions. The method we study in this dissertation, is a standard application of Clifford theory, made efficient by the use of Fischer-Clifford matrices, as introduced by Fischer. We consider only extensions Ḡ of the normal subgroup N by the subgroup G with the property that every irreducible character of N can be extended to an irreducible character of its inertia group in Ḡ , if N is abelian. This is indeed the case if Ḡ is a split extension, by a well known theorem of Mackey. A brief outline of the classical theory of characters pertinent to this study, is followed by a discussion on the calculation of the conjugacy classes of extension groups by the method of coset analysis. The Clifford theory which provide the basis for the theory of Fischer-Clifford matrices is discussed in detail. Some of the properties of these Fischer-Clifford matrices which make their calculation much easier, are also given. We restrict ourselves to split extension groups Ḡ = N:G in which N is always an elementary abelian 2-group. In this thesis we are concerned with the construction of the character tables (by means of the technique of Fischer-Clifford matrices) of certain extension groups which are associated with the orthogonal group O+10(2), the automorphism groups U₆(2):2, U₆(2):3 of the unitary group U₆(2) and the smallest Fischer sporadic simple group Fi₂₂. These groups are of the type type 2⁸:(U₄(2):2), (2⁹ : L₃(4)):2, (2⁹:L₃(4)):3 and 2⁶:(2⁵:S₆).
Benaych-Georges, Florent. "Matrices aléatoires et probabilités libres." Paris 6, 2005. http://www.theses.fr/2005PA066566.
Full textPorto, Julianna Pinele Santos 1990. "Geometria da informação : métrica de Fisher." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307256.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
Made available in DSpace on 2018-08-23T13:44:50Z (GMT). No. of bitstreams: 1 Porto_JuliannaPineleSantos_M.pdf: 2346170 bytes, checksum: 9f8b7284329ef1eb2f319c2e377b7a3c (MD5) Previous issue date: 2013
Resumo: A Geometria da Informação é uma área da matemática que utiliza ferramentas geométricas no estudo de modelos estatísticos. Em 1945, Rao introduziu uma métrica Riemanniana no espaço das distribuições de probabilidade usando a matriz de informação, dada por Ronald Fisher em 1921. Com a métrica associada a essa matriz, define-se uma distância entre duas distribuições de probabilidade (distância de Rao), geodésicas, curvaturas e outras propriedades do espaço. Desde então muitos autores veem estudando esse assunto, que está naturalmente ligado a diversas aplicações como, por exemplo, inferência estatística, processos estocásticos, teoria da informação e distorção de imagens. Neste trabalho damos uma breve introdução à geometria diferencial e Riemanniana e fazemos uma coletânea de alguns resultados obtidos na área de Geometria da Informação. Mostramos a distância de Rao entre algumas distribuições de probabilidade e damos uma atenção especial ao estudo da distância no espaço formado por distribuições Normais Multivariadas. Neste espaço, como ainda não é conhecida uma fórmula fechada para a distância e nem para a curva geodésica, damos ênfase ao cálculo de limitantes para a distância de Rao. Conseguimos melhorar, em alguns casos, o limitante superior dado por Calvo e Oller em 1990
Abstract: Information Geometry is an area of mathematics that uses geometric tools in the study of statistical models. In 1945, Rao introduced a Riemannian metric on the space of the probability distributions using the information matrix provided by Ronald Fisher in 1921. With the metric associated with this matrix, we define a distance between two probability distributions (Rao's distance), geodesics, curvatures and other properties. Since then, many authors have been studying this subject, which is associated with various applications, such as: statistical inference, stochastic processes, information theory, and image distortion. In this work we provide a brief introduction to Differential and Riemannian Geometry and a survey of some results obtained in Information Geometry. We show Rao's distance between some probability distributions, with special atention to the study of such distance in the space of multivariate normal distributions. In this space, since closed forms for the distance and for the geodesic curve are not known yet, we focus on the calculus of bounds for Rao's distance. In some cases, we improve the upper bound provided by Calvo and Oller in 1990
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Matematica Aplicada
Mestra em Matemática Aplicada
Books on the topic "Matrice de Fisher"
Abujabal, Hamza Ali Sanousi. On the Fischer Matrices for some extensions of 2-groups. Birmingham: University of Birmingham, 1987.
Find full textCheng, Russell. The Skew Normal Distribution. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0012.
Full textAlden, Maureen. The Oresteia Story in the Odyssey. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199291069.003.0003.
Full textBook chapters on the topic "Matrice de Fisher"
Mingo, James A., and Roland Speicher. "Free Entropy χ ∗: The Non-microstates Approach via Free Fisher Information." In Free Probability and Random Matrices, 195–223. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6942-5_8.
Full textNel, D. G., and P. C. N. Groenewald. "On a Fisher—Cornish Type Expansion of Wishart Matrices." In Innovations in Multivariate Statistical Analysis, 223–32. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4603-0_16.
Full textDesai, Tejas. "On Testing Equality of Covariance Matrices." In A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem, 17–29. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6443-3_3.
Full textHeavens, A. "Fisher Matrices and All That: Experimental Design and Data Compression." In Data Analysis in Cosmology, 51–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-44767-2_2.
Full textGepperth, Alexander, and Florian Wiech. "Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks." In Lecture Notes in Computer Science, 481–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3_39.
Full textZinn-Justin, Jean. "Critical phenomena: The field theory approach." In From Random Walks to Random Matrices, 81–100. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198787754.003.0006.
Full text"ON THE CORNISH-FISHER EXPANSION IN FINITE POPULATION." In Multivariate Statistics and Matrices in Statistics, 35–42. De Gruyter, 1995. http://dx.doi.org/10.1515/9783112314210-004.
Full textBasor, Estelle L. "Toeplitz determinants, Fisher-Hartwig symbols, and random matrices." In Recent Perspectives in Random Matrix Theory and Number Theory, 309–36. Cambridge University Press, 2005. http://dx.doi.org/10.1017/cbo9780511550492.012.
Full textZhang, David, Xiao-Yuan Jing, and Jian Yang. "2D Image Matrix-Based Discriminator." In Computational Intelligence and its Applications, 258–86. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-830-7.ch011.
Full textSelli, Serkan, Onur Sevindik, Gamze Guclu, and Jing Zhao. "Flavour of Fish and Fish Proteins." In Flavour and Consumer Perception of Food Proteins, 119–49. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781839165047-00119.
Full textConference papers on the topic "Matrice de Fisher"
Peeters, Ralf L. M., and Bernard Hanzon. "Symbolic computation of fisher information matrices." In 1997 European Control Conference (ECC). IEEE, 1997. http://dx.doi.org/10.23919/ecc.1997.7082483.
Full textCosta, S. I. R., S. A. Santos, and J. E. Strapasson. "Fisher information matrix and hyperbolic geometry." In IEEE Information Theory Workshop, 2005. IEEE, 2005. http://dx.doi.org/10.1109/itw.2005.1531851.
Full textALLAHDADIAN, SAEID, MICHAEL DÖHLER, CARLOS VENTURA, and LAURENT MEVEL. "Hierarchical Fisher-information-matrix-based Clustering." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32478.
Full textZheng, Zhonglong, Mudan Yu, Jiong Jia, Huawen Liu, Haixin Zhang, Fangmei Fu, and Xiaoqiao Huang. "Fisher Discrimination Based Low Rank Matrix Recovery." In 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.48.
Full textAgarwal, A. "Large N Matrix Models and Noncommutative Fisher Information." In THEORETICAL PHYSICS: MRST 2002: A Tribute to George Leibbrandt. AIP, 2002. http://dx.doi.org/10.1063/1.1524569.
Full textWang, Zhan, and Gamini Dissanayake. "Observability analysis of SLAM using fisher information matrix." In 2008 10th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2008. http://dx.doi.org/10.1109/icarcv.2008.4795699.
Full textZhang, Yong, and Jianhu Guo. "Weighted Fisher Non-negative Matrix Factorization for Face Recognition." In 2009 Second International Symposium on Knowledge Acquisition and Modeling. IEEE, 2009. http://dx.doi.org/10.1109/kam.2009.320.
Full textAli, Muhammad, Michael Antolovich, and Boyue Wang. "Density estimation on Stiefel manifolds using matrix-Fisher model." In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2016. http://dx.doi.org/10.1109/cisp-bmei.2016.7852683.
Full textLei, Ming, Christophe Baehr, and Pierre Del Moral. "Fisher information matrix-based nonlinear system conversion for state estimation." In 2010 8th IEEE International Conference on Control and Automation (ICCA). IEEE, 2010. http://dx.doi.org/10.1109/icca.2010.5524066.
Full textGao, Ruizhou, Yunhong Ma, Yimin Zhang, and Xinyi Li. "Target Combat Intention Recognition Based on Improved Fisher Information Matrix." In 2023 International Conference on Cyber-Physical Social Intelligence (ICCSI). IEEE, 2023. http://dx.doi.org/10.1109/iccsi58851.2023.10303971.
Full textReports on the topic "Matrice de Fisher"
Ortiz, M. Analytical Methods of Approximating the Fisher Information Matrix for the Lognormal Distribution. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1557955.
Full textWadman, Heidi, and Jesse McNinch. Spatial distribution and thickness of fine-grained sediment along the United States portion of the upper Niagara River, New York. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41666.
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