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Hessian Structures on Deformed Exponential Families

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Geometric Science of Information (GSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8085))

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Abstract

A deformed exponential family is a generalization of exponential families. It is known that an exponential family naturally has dualistic Hessian structures, and its canonical divergence coincides with the Kullback-Leibler divergence, which is also called the relative entropy. On the other hand, a deformed exponential family naturally has two kinds of dualistic Hessian structures. In this paper, such Hessian structures are summarized and a generalized relative entropy is constructed from the viewpoint of estimating functions.

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References

  1. Amari, S., Nagaoka, H.: Method of Information Geometry. In: Amer. Math. Soc., Providence. Oxford University Press, Oxford (2000)

    Google Scholar 

  2. Amari, S., Ohara, A., Matsuzoe, H.: Geometry of deformed exponential families: invariant, dually-flat and conformal geometry. Physica A 391, 4308–4319 (2012)

    Article  MathSciNet  Google Scholar 

  3. Lauritzen, S.L.: Statistical manifolds. In: Differential Geometry in Statistical Inferences, Hayward California. IMS Lecture Notes Monograph Series, vol. 10, pp. 96–163 (1987)

    Google Scholar 

  4. Matsuzoe, H.: Statistical manifolds and geometry of estimating functions. In: The 3rd International Colloquium on Differential Geometry and its Related Fields (in press)

    Google Scholar 

  5. Matsuzoe, H., Ohara, A.: Geometry for q-exponential families. In: Recent Progress in Differential Geometry and Its Related Fields, pp. 55–71. World Sci. Publ. (2011)

    Google Scholar 

  6. Murata, N., Takenouchi, T., Kanamori, T., Eguchi, S.: Information geometry of U-boost and Bregman divergence. Neural Comput. 16, 1437–1481 (2004)

    Article  MATH  Google Scholar 

  7. Naudts, J.: Generalised Thermostatistics. Springer (2011)

    Google Scholar 

  8. Ohara, A., Wada, T.: Information geometry of q-Gaussian densities and behaviors of solutions to related diffusion equations. J. Phys. A: Math. Theor. 43, No.035002 (2010)

    Google Scholar 

  9. Shima, H.: The Geometry of Hessian Structures. World Scientific (2007)

    Google Scholar 

  10. Tsallis, C.: Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World. Springer, New York (2009)

    Google Scholar 

  11. Vigelis, R.F., Cavalcante, C.C.: On φ-Families of Probability Distributions. J. Theor. Probab. 21, 1–25 (2011)

    Google Scholar 

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Matsuzoe, H., Henmi, M. (2013). Hessian Structures on Deformed Exponential Families. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_29

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  • DOI: https://doi.org/10.1007/978-3-642-40020-9_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40019-3

  • Online ISBN: 978-3-642-40020-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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