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Books on the topic 'Model matriciel'

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Published: 4 June 2021
Last updated: 17 February 2022

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1

Differential equations: Matrices and models. Englewood Cliffs, N.J: Prentice Hall, 1995.

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2

Bleher, Pavel. Random matrices and the six-vertex model. Providence, Rhode Island, USA: American Mathematical Society, 2014.

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3

Les matrices culturelles. Paris: Hermann, 2008.

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4

Kehoe, Timothy Jerome. Social accounting matrices and applied general equilibrium models. [Minneapolis, Minn.]: Federal Reserve Bank of Minneapolis, 1996.

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5

Burrill, Gail F. Advanced modeling and matrices. Orangeburg, N.Y: Dale Seymour, 1998.

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6

Kandasamy, W. B. Vasantha. Super fuzzy matrices and super fuzzy models for social scientists. Ann Arbor: InfoLearnQuest, 2008.

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7

Kuijlaars, Arno B. J., 1963- and Mo Man Yue, eds. The Hermitian two matrix model with an even quartic potential. Providence, R.I: American Mathematical Society, 2011.

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8

Neuts, Marcel F. Matrix-geometric solutions in stochastic models: An algorithmic approach. New York: Dover Publications, 1994.

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9

Matrices and graphs: Stability problems in mathematical ecology. Boca Raton: CRC Press, 1993.

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10

1962-, Feng Zunde, and Liu Jimin 1963-, eds. Bing tai xi tong fen xi li lun ji qi zai ce liang zhong de ying yong: Analysis theory on ill-conditioned system with application in surveying. Beijing: Ce hui chu ban she, 2007.

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11

Breisinger, Clemens. Social accounting matrices and multiplier analysis: An introduction with exercises. Washington, D.C: International Food Policy Research Institute, 2009.

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12

Breisinger, Clemens. Social accounting matrices and multiplier analysis: An introduction with exercises. Washington, D.C: International Food Policy Research Institute, 2009.

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13

Breisinger, Clemens. Social accounting matrices and multiplier analysis: An introduction with exercises. Washington, D.C: International Food Policy Research Institute, 2009.

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14

Mauro, Grassi, ed. Matrici e modelli i/o regionali: Il caso della Toscana. Firenze: F. Angeli, 1985.

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15

Jones, Matthew T. Estimating Markov transition matrices using proportions data: An application to credit risk. [Washington, D.C.]: International Monetary Fund, Monetary and Financial Systems Dept., 2005.

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16

Applied matrix models: A second course in linear algebra with computer applications. New York: Wiley, 1985.

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17

Peterson, R. Neal. A single equation approach to estimating nonstationary Markov matrices: The case of U.S. Agriculture, 1974-78. Washington, DC: U.S. Dept. of Agriculture, Economic Research Service, 1990.

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18

International Conference on Matrix-Analytic Methods in Stochastic Models (2nd 1998 Winnipeg, Man.). Advances in matrix-analytic methods for stochastic models. Neshanic Station, NJ: Notable Publications, 1998.

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19

Matrix population models: Construction, analysis, and interpretation. Sunderland, Mass: Sinauer Associates, 1989.

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20

Institute of Policy Studies (Colombo, Sri Lanka), ed. A framework for social accounting matrices (SAMs) for Sri Lanka. Colombo: Institute of Policy Studies, 2006.

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21

Piech, Henryk. Researches [sic] of [sic] improving consistent judgement in Saaty matrix. Częstochowa: Publ. Office of Czestochowa University of Technology, 2000.

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22

1929-, Kato Y., ed. Dynamics of one-dimensional quantum systems: Inverse-square interaction models. Cambridge, UK: Cambridge University Press, 2009.

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23

Faria, J. A. Brandão. Multiconductor transmission-line structures: Modal analysis techniques. New York: Wiley, 1993.

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24

Branch, Canada Defence Research Establishment Atlantic Research and Development. A surface panel method for the calculation of added mass matrices for finite element models. S.l: s.n, 1988.

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25

Performance of communication systems: A model-based approach with matrix-geometric methods. Berlin: Springer, 2001.

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26

Ziegler, Hartmut. Die Erzeugung von Verkehrsverflechtungsmatrizen aus Querschnittszählungen zur vereinfachten Ermittlung von Verkehrsumlagerungen. Aachen: Institut für Stadtbauwesen, RWTH Aachen, 1989.

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27

Vinokur, Marcel. Flux Jacobian matrices and generalized Roe average for an equilibrium real gas. Washington, D. C: NASA, 1988.

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28

service), SpringerLink (Online, ed. Linear Algebra and Linear Models. 3rd ed. London: Springer London, 2012.

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29

Drud, Arne. The transaction value approach: A systematic method of defining economywide models based on social accounting matrices. Washington, D.C., U.S.A. (1818 H St., N.W., Washington 20433): World Bank, 1985.

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30

Luca, Benvenuti, De Santis Alberto, and Farina Lorenzo 1963-, eds. Positive systems: Proceedings of the First Multidisciplinary International Symposium on Positive Systems, Theory and Applications (POSTA 2003), Rome, Italy, August 28-30, 2003. Berlin: Springer, 2003.

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31

Multidisciplinary International Symposium on Positive systems: Theory and Applications (2nd 2006 Grenoble, France). Positive systems: Proceedings of the second Multidisciplinary International Symposium on Positive Systems: Theory and Applications (POSTA 06), Grenoble, France, Aug. 30-31, Sept. 1, 2006. Berlin: Springer, 2006.

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32

International Conference on Matrix Analytic Methods (3rd 2000 Leuven, Belgium). Advances in algorithmic methods for stochastic models: Proceedings of the 3rd International Conference on Matrix Analytic Methods. Neshanic Station, NJ: Notable Publications, 2000.

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33

Grafarend, Erik. Linear and Nonlinear Models: Fixed effects, random effects, and total least squares. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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34

Majumdar, Satya N. Random growth models. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.38.

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This article discusses the connection between a particular class of growth processes and random matrices. It first provides an overview of growth model, focusing on the TASEP (totally asymmetric simple exclusion process) with parallel updating, before explaining how random matrices appear. It then describes multi-matrix models and line ensembles, noting that for curved initial data the spatial statistics for large time t is identical to the family of largest eigenvalues in a Gaussian Unitary Ensemble (GUE multi-matrix model. It also considers the link between the line ensemble and Brownian motion, and whether this persists on Gaussian Orthogonal Ensemble (GOE) matrices by comparing the line ensembles at fixed position for the flat polynuclear growth model (PNG) and at fixed time for GOE Brownian motions. Finally, it examines (directed) last passage percolation and random tiling in relation to growth models.
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35

Bugl, Paul. Differential Equations: Matrices and Models. Prentice Hall, 1994.

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36

Bugl, Paul. Differential Equations: Matrices and Models. Prentice Hall, 1994.

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37

Bleher, Pavel, and Alexander Its. Random Matrix Models and Their Applications. University of Cambridge ESOL Examinations, 2011.

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38

Comtet1, Alain, and Yves Tourigny2. Impurity models and products of random matrices. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0011.

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This is an introduction to the theory of one-dimensional disordered systems and products of random matrices, confined to the 2×2 case. The notion of impurity model—that is, a system in which the interactions are highly localized—links the two themes and enables their study by elementary mathematical tools. After discussing the spectral theory of some impurity models, Furstenberg’s theorem is stated and illustrated, which gives sufficient conditions for the exponential growth of a product of independent, identically distributed matrices.
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39

Matrix Model: The 7 Matrices of Neuro-Semantics. Neuro-Semantic Publications, 2002.

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40

Eynard, Bertrand. Random matrices and loop equations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0007.

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This chapter is an introduction to algebraic methods in random matrix theory (RMT). In the first section, the random matrix ensembles are introduced and it is shown that going beyond the usual Wigner ensembles can be very useful, in particular by allowing eigenvalues to lie on some paths in the complex plane rather than on the real axis. As a detailed example, the Plancherel model is considered from the point of RMT. The second section is devoted to the saddle-point approximation, also called the Coulomb gas method. This leads to a system of algebraic equations, the solution of which leads to an algebraic curve called the ‘spectral curve’ which determines the large N expansion of all observables in a geometric way. Finally, the third section introduces the ‘loop equations’ (i.e., Schwinger–Dyson equations associated with matrix models), which can be solved recursively (i.e., order by order in a semi-classical expansion) by a universal recursion: the ‘topological recursion’.
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41

Forrester, Peter. Wigner matrices. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.21.

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This article reviews some of the important results in the study of the eigenvalues and the eigenvectors of Wigner random matrices, that is. random Hermitian (or real symmetric) matrices with iid entries. It first provides an overview of the Wigner matrices, introduced in the 1950s by Wigner as a very simple model of random matrices to approximate generic self-adjoint operators. It then considers the global properties of the spectrum of Wigner matrices, focusing on convergence to the semicircle law, fluctuations around the semicircle law, deviations and concentration properties, and the delocalization of the eigenvectors. It also describes local properties in the bulk and at the edge before concluding with a brief analysis of the known universality results showing how much the behaviour of the spectrum is insensitive to the distribution of the entries.
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42

Bouchaud, Jean-Phillipe, and Marc Potters. Asymptotic singular value distributions in information theory. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.41.

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This article examines asymptotic singular value distributions in information theory, with particular emphasis on some of the main applications of random matrices to the capacity of communication channels. Results on the spectrum of random matrices have been adopted in information theory. Furthermore, information theorists, motivated by certain channel models, have obtained a number of new results in random matrix theory (RMT). Most of those results are related to the asymptotic distribution of the (square of) the singular values of certain random matrices that model data communication channels. The article first provides an overview of three transforms that are useful in expressing the asymptotic spectrum results — Stieltjes transform, η-transform, and Shannon transform — before discussing the main results on the limit of the empirical distributions of the eigenvalues of various random matrices of interest in information theory.
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43

Burda, Zdzislaw, and Jerzy Jurkiewicz. Phase transitions. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.14.

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This article considers phase transitions in matrix models that are invariant under a symmetry group as well as those that occur in some matrix ensembles with preferred basis, like the Anderson transition. It first reviews the results for the simplest model with a nontrivial set of phases, the one-matrix Hermitian model with polynomial potential. It then presents a view of the several solutions of the saddle point equation. It also describes circular models and their Cayley transform to Hermitian models, along with fixed trace models. A brief overview of models with normal, chiral, Wishart, and rectangular matrices is provided. The article concludes with a discussion of the curious single-ring theorem, the successful use of multi-matrix models in describing phase transitions of classical statistical models on fluctuating two-dimensional surfaces, and the delocalization transition for the Anderson, Hatano-Nelson, and Euclidean random matrix models.
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44

Gruber, Marvin H. J. Matrix Algebra for Linear Models. Wiley & Sons, Incorporated, John, 2013.

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45

Gruber, Marvin H. J. Matrix Algebra for Linear Models. Wiley & Sons, Incorporated, John, 2013.

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46

Matrix Algebra For Linear Models. John Wiley & Sons Inc, 2014.

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47

Bugl, Paul. Student Solutions Manual: Differential Equations: Matrices and Models. Prentice Hall College Div, 1995.

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48

(Editor), Pavel Bleher, and Alexander Its (Editor), eds. Random Matrix Models and Their Applications. Cambridge University Press, 2001.

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49

1947-, Bleher Pavel, and Its Alexander R, eds. Random matrix models and their applications. Cambridge, U.K: Cambridge University Press, 2001.

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50

Bai, Zhidong, Ying-Chang Liang, and Zhaoben Fang. Spectral Theory of Large Dimensional Random Matrices and Its Applications to Wireless Communications and Finance Statistics: Random Matrix Theory and Its Applications. World Scientific Publishing Co Pte Ltd, 2014.

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