Skip to main content

Fisher-Rao Riemannian Geometry of Equivalent Gaussian Measures on Hilbert Space

  • Conference paper
  • First Online:
Geometric Science of Information (GSI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14071))

Included in the following conference series:

Abstract

This work presents an explicit description of the Fisher-Rao Riemannian metric on the Hilbert manifold of equivalent centered Gaussian measures on an infinite-dimensional Hilbert space. We show that the corresponding quantities from the finite-dimensional setting of Gaussian densities on Euclidean space, including the Riemannian metric, Levi-Civita connection, curvature, geodesic curve, and Riemannian distance, when properly formulated, directly generalize to this setting. Furthermore, we discuss the connection with the Riemannian geometry of positive definite unitized Hilbert-Schmidt operators on Hilbert space, which can be viewed as a regularized version of the current setting.

This research is partially supported by JSPS KAKENHI Grant Number JP20H04250.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Amari, S., Nagaoka, H.: Methods of Information Geometry. American Mathematical Society (2000)

    Google Scholar 

  2. Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Information geometry and sufficient statistics. Prob. Theory Relat. Fields 162, 327–364 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  3. Ay, N., et al.: Parametrized measure models. Bernoulli 24(3), 1692–1725 (2018)

    Google Scholar 

  4. Ay, N., Jost, J., Lê, H.V., Schwachhöfer, L.: Information Geometry. EMGFASMSM, vol. 64. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56478-4

    Book  MATH  Google Scholar 

  5. Bhatia, R.: Positive Definite Matrices. Princeton University Press, Princeton (2007)

    MATH  Google Scholar 

  6. Bogachev, V.: Gaussian Measures. American Mathematical Society (1998)

    Google Scholar 

  7. Cena, A., Pistone, G.: Exponential statistical manifold. Ann. Inst. Stat. Math. 59, 27–56 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Da Prato, G., Zabczyk, J.: Second Order Partial Differential Equations in Hilbert Spaces, vol. 293. Cambridge University Press, Cambridge (2002)

    Book  MATH  Google Scholar 

  9. Feldman, J.: Equivalence and perpendicularity of Gaussian processes. Pac. J. Math. 8(4), 699–708 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  10. Felice, D., Hà Quang, M., Mancini, S.: The volume of Gaussian states by information geometry. J. Math. Phys. 58(1), 012201 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  11. Fisher, R.A.: On the mathematical foundations of theoretical statistics. Phil. Trans. Roy. Soc. Lond. Ser. A 222, 309–368 (1922)

    Article  MATH  Google Scholar 

  12. Gibilisco, P., Pistone, G.: Connections on non-parametric statistical manifolds by orlicz space geometry. Infinite Dimen. Anal. Quant. Prob. Relat. Topics 1(02), 325–347 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  13. Hájek, J.: On a property of normal distributions of any stochastic process. Czechoslovak Math. J. 08(4), 610–618 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kadison, R., Ringrose, J.: Fundamentals of the Theory of Operator Algebras. Volume I: Elementary Theory. Academic Press, Cambridge (1983)

    Google Scholar 

  15. Lang, S.: Fundamentals of Differential Geometry, vol. 191. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4612-0541-8

    Book  MATH  Google Scholar 

  16. Larotonda, G.: Nonpositive curvature: a geometrical approach to hilbert-schmidt operators. Differ. Geom. Appl. 25, 679–700 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  17. Lenglet, C., Rousson, M., Deriche, R., Faugeras, O.: Statistics on the manifold of multivariate normal distributions: theory and application to diffusion tensor MRI processing. J. Math. Imaging Vision 25(3), 423–444 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  18. Minh, H.Q.: Estimation of Riemannian distances between covariance operators and Gaussian processes (2021). arXiv preprint arXiv:2108.11683

  19. Minh, H.: Regularized divergences between covariance operators and Gaussian measures on Hilbert spaces. J. Theor. Prob. 34, 580–643 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  20. Newton, N.J.: An infinite-dimensional statistical manifold modelled on Hilbert space. J. Funct. Anal. 263(6), 1661–1681 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. Int. J. Comput. Vision 66(1), 41–66 (2006)

    Article  MATH  Google Scholar 

  22. Petryshyn, W.: Direct and iterative methods for the solution of linear operator equations in Hilbert spaces. Trans. Am. Math. Soc. 105, 136–175 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  23. Pistone, G., Rogantin, M.P.: The exponential statistical manifold: mean parameters, orthogonality and space transformations. In: Bernoulli, pp. 721–760 (1999)

    Google Scholar 

  24. Pistone, G., Sempi, C.: An infinite-dimensional geometric structure on the space of all the probability measures equivalent to a given one. In: The Annals of Statistics, pp. 1543–1561 (1995)

    Google Scholar 

  25. Rao, C.: Information and accuracy attainable in the estimation of statistical parameters. Bull. Calcutta Math. Soc. 37(3), 81–91 (1945)

    MathSciNet  MATH  Google Scholar 

  26. Reed, M., Simon, B.: Methods of Modern Mathematical Physics: Functional Analysis. Academic Press, Cambridge (1975)

    MATH  Google Scholar 

  27. Simon, B.: Notes on infinite determinants of Hilbert space operators. Adv. Math. 24, 244–273 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  28. Skovgaard, L.T.: A Riemannian geometry of the multivariate normal model. Scand. J. Stat. 11, 211–223 (1984)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hà Quang Minh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Minh, H.Q. (2023). Fisher-Rao Riemannian Geometry of Equivalent Gaussian Measures on Hilbert Space. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2023. Lecture Notes in Computer Science, vol 14071. Springer, Cham. https://doi.org/10.1007/978-3-031-38271-0_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-38271-0_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-38270-3

  • Online ISBN: 978-3-031-38271-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics