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Geometry of the probability simplex and its connection to the maximum entropy method Cover

Geometry of the probability simplex and its connection to the maximum entropy method

By: H. Gzyl and  F. Nielsen  
Open Access
|Jul 2020

References

  1. [1] Arsigny, V., Fillard, P., Pennec, X. and Ayach, N. (2007). Geometric Means in a Novel Vector Space Structure on Symmetric positive definite matrices, SIAM J. Matrix Theory, 29, 328-347.
  2. [2] Amari, S.-i. “Differential Geometric Methods in Statistics”, Lecture Notes in Statistics, 28, Berlin (1985).10.1007/978-1-4612-5056-2
  3. [3] Amari, S.-i. “Information Geometry and its Applications”, Springer (2016).10.1007/978-4-431-55978-8
  4. [4] Amari, S.-i., Barndorff-Nielsen, O., Kass, R., Lauritzen, S. and Rao, C. “Differential Geometry in Statistical Inference” Institute of Mathematical Statistics Lecture Notes, Monograph Series, Vol. 10, Hayward, (1987).10.1214/lnms/1215467060
  5. [5] Barndorff-Nielsen, O. “Information and Exponential Families in Statistical Theory”, Chichester, Wiley (1978).
  6. [6] Brown, C.C. (1985) Entropy increase and measure theory, Proc. Am. Math. Soc., 95, 488-450.
  7. [7] Casalis, M. (1991) Familles exponentielles naturelles surRd invariantes par un groupe. International Statistical Review/Revue Internationale de Statistique, 241-262.
  8. [8] Calin, O. and Udriste, C. Geometric Modeling in Probability and Statistics, Springer Internl. Pub., Switzerland, (2010).
  9. [9] Efron, B. (1978) The geometry of exponential families. The Annals of Statistics, 6(2), 362-376.10.1214/aos/1176344130
  10. [10] Gzyl, H. and Recht, L. (2006) “A geometry in the space of probabilities II: Projective spaces and exponential families” Rev. Iberoamericana de Matemáticas, 22, 833-850.10.4171/RMI/475
  11. [11] Gzyl, H. and Recht, L. (2007) Intrinsic geometry on the class of probability densities and exponential families, Public. Mathematiques, 51, 309-322.
  12. [12] Gzyl, H. (2019) Best predictors in logarithmic distance between positive random variables. To appear Journal of Applied Mathematics, Statistics and Informatics, 15, 15-2810.2478/jamsi-2019-0006
  13. [13] Hotelling, H (1930) Spaces of statistical parameters. Bulletin of the American Mathematical Society (AMS), 36:191
  14. [14] Imparato, D. and Trivelato, B. Geometry of extended exponential models, in Algebraic and Geometric Methods in Statistics, Gibilisco, P., Riccomagno, E. Rogantin, M.P. and Wynn, H. eds., Cambridge Univ. Press, Cambridge, (2010).
  15. [15] Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical review, 106(4), 620.10.1103/PhysRev.106.620
  16. [16] Klein, M. (1956) Entropy and the Ehrenfest urn model, Physica, 22, 569-575,10.1016/S0031-8914(56)90001-5
  17. [17] Lang, S. Math talks for undergraduates, Springer, New York, (1999).10.1007/978-1-4612-1476-2
  18. [18] Lawson, J.D. and Lim, Y. (2001) The Geometric mean, matrices, metrics and more, Amer. Math.,Monthly, 108, 797-812.10.1080/00029890.2001.11919815
  19. [19] Li, F., Zhang L. and Zhang Z. (2018) Lie Group Machine Learning, Walter de Gruyter GmbH & Co KG, ISBN9783110499506.10.1515/9783110499506
  20. [20] Moakher, M. (2005) A differential geometric approach to the geometric mean of symmetric positive definite matrices, SIAM. J. Matrix Anal. & Appl., 26, 735-747
  21. [21] Moran, P.A.P. (1960) Entropy, Markov processes and Boltzmann’s H-theorem, Proc. Camb. Phil. Soc., 57, 833-842.
  22. [22] Nielsen, F., and Sun, K. (2017). Clustering in Hilbert simplex geometry. preprint arXiv:1704.00454.
  23. [23] Nielsen, F., and Nock R. (2018) On the geometry of mixtures of prescribed distributions, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).10.1109/ICASSP.2018.8461869
  24. [24] Pistone, G. Algebraic varieties vs. differentiable manifolds, in Algebraic and Geometric Methods in Statistics, Gibilisco, P., Riccomagno, E. Rogantin, M.P. and Wynn, H. eds., Cambridge Univ. Press, Cambridge, (2010).10.1017/CBO9780511642401.022
  25. [25] Pistone, G. and Rogantin, M.P. The exponential statistical manifold: mean parameters, orthogonality and space transformations” Bernoulli, 5 (1999), 721-760.10.2307/3318699
  26. [26] Pistone, G. and Sempi, C. “An infinite dimensional geometric structure in the space of all probability measures equivalent to a given one”. Ann. Statist., 23 (1995), 1543-1561.
  27. [27] Rao, C. R. (1992). Information and the accuracy attainable in the estimation of statistical parameters. In Breakthroughs in statistics (pp. 235-247). Springer, New York, NY.10.1007/978-1-4612-0919-5_16
  28. [28] Schwartzmazn, A. (2015) Lognormal distribution and geometric averages of positive definite matrices, Int. Stat. Rev., 84, 456-486.
  29. [29] Vajda, I. “Theory of statistical inference and information” Kluwer Acad., Dordrecht, (1989).
DOI: https://doi.org/10.2478/jamsi-2020-0003 | Journal eISSN: 1339-0015 | Journal ISSN: 1336-9180
Language: English
Page range: 25 - 35
Published on: Jul 9, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2020 H. Gzyl, F. Nielsen, published by University of Ss. Cyril and Methodius in Trnava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.