Yıl 2020,
Cilt: 49 Sayı: 1, 120 - 135, 06.02.2020
Vladimir Balan
,
E. Peyghan
,
Esa Sharahi
Kaynakça
- [1] S. Amari, Information geometry of the EM and em algorithms for neural networks,
Neural Networks, 8 (9), 1379–1408, 1995.
- [2] S. Amari and H. Nagaoka, Methods of information geometry, American Mathematical
Society, 2000.
- [3] V. Balan, H.V. Grushevskaya,N.G. Krylova, G.G. Krylov, and I.V. Lipnevich, Two-
dimensional first-order phase transition as signature change event in contact statisti-
cal manifolds with Finsler metric, Appl. Sci. 21, 11–26, 2019.
- [4] M. Belkin, P. Niyogi and V.Sindhwani, Manifold regularization: a geometric frame-
work for learning from labeled and unlabeled examples, J. Mach. Learn. Res. 7, 2399–
2434, 2006.
- [5] M. Burgin, Theory of information: fundamentality, diversity and unification, World
Scientific Series in Information Studies, 2009.
- [6] A. Caticha, Geometry from information geometry, https://arxiv.org/abs/1512.
09076v1.
- [7] A. Caticha, The information geometry of space and time, https://arxiv.org/abs/
gr-qc/0508108.
- [8] N.N. Cencov, Statistical decision rules and optimal inference, Amer. Mathematical
Society: Translations of Mathematical Monographs, 1982.
- [9] R.A. Fisher, On the mathematical foundations of theoretical statistics, Philos. Trans.
R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 222, 309–368, 1922.
- [10] H. Furuhata, Hypersurfaces in statistical manifolds, Diff. Geom. Appl. 27, 420–429,
2009.
- [11] H. Furuhata, I. Hasegawa, Y. Okuyama, K. Sato and M.H. Shahid, Sasakian statistical
manifolds, J. Geom. Phys. 117, 179–186, 2017.
- [12] S. Ianuş, Statistical manifolds and tangent bundles, Sci. Bull. Univ. Politechnica of
Bucharest Ser. D, 56, 29–34, 1994.
- [13] S. Lauritzen, Statistical manifolds, in: Differential Geometry in Statistical Inference,
IMS Lecture Notes 10, 163–216, 1987.
- [14] H. Matsuzoe and J.I. Inoguchi, Statistical structures on tangent bundles, APPS. Appl.
Sci. 5 (1), 55–57, 2003.
- [15] J.M. Oller and C.N. Caudras, Rao’s distance for negative multinomial distributions,
Sankhya A, 47 (1), 75–83, 1985.
- [16] C.R. Rao, Information and accuracy attainable in estimation of statistical parameters,
Bull. Cal. Math. Soc. 37, 81–91, 1945.
- [17] K. Sun and S. Marchand-Maillet, An information geometry of statistical manifold
learning, Proceedings of the 31st International Conference on Machine Learning
(ICML-14), 1–9, 2014.
- [18] K. Yano and S. Ishihara, Tangent and cotangent bundles, Marcel Dekker Inc., New
York, 1973.
Statistical structures on the tangent bundle of a statistical manifold with Sasaki metric
Yıl 2020,
Cilt: 49 Sayı: 1, 120 - 135, 06.02.2020
Vladimir Balan
,
E. Peyghan
,
Esa Sharahi
Öz
The first part of the paper is devoted to the classification of the statistical structures which live on the tangent bundle of a statistical manifold endowed with a Sasaki metric. Further, considering a Kähler structure on the base statistical manifold, we introduce a family of almost complex structures on the tangent bundle equipped with the Sasaki metric, and find equivalent conditions for which this family induces a Kähler structure. Finally, we derive equivalent conditions for existence of holomorphic structures on the tangent bundle equipped with the Sasaki metric in the presence of a statistical structure. Several illustrative examples are provided, as well.
Kaynakça
- [1] S. Amari, Information geometry of the EM and em algorithms for neural networks,
Neural Networks, 8 (9), 1379–1408, 1995.
- [2] S. Amari and H. Nagaoka, Methods of information geometry, American Mathematical
Society, 2000.
- [3] V. Balan, H.V. Grushevskaya,N.G. Krylova, G.G. Krylov, and I.V. Lipnevich, Two-
dimensional first-order phase transition as signature change event in contact statisti-
cal manifolds with Finsler metric, Appl. Sci. 21, 11–26, 2019.
- [4] M. Belkin, P. Niyogi and V.Sindhwani, Manifold regularization: a geometric frame-
work for learning from labeled and unlabeled examples, J. Mach. Learn. Res. 7, 2399–
2434, 2006.
- [5] M. Burgin, Theory of information: fundamentality, diversity and unification, World
Scientific Series in Information Studies, 2009.
- [6] A. Caticha, Geometry from information geometry, https://arxiv.org/abs/1512.
09076v1.
- [7] A. Caticha, The information geometry of space and time, https://arxiv.org/abs/
gr-qc/0508108.
- [8] N.N. Cencov, Statistical decision rules and optimal inference, Amer. Mathematical
Society: Translations of Mathematical Monographs, 1982.
- [9] R.A. Fisher, On the mathematical foundations of theoretical statistics, Philos. Trans.
R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 222, 309–368, 1922.
- [10] H. Furuhata, Hypersurfaces in statistical manifolds, Diff. Geom. Appl. 27, 420–429,
2009.
- [11] H. Furuhata, I. Hasegawa, Y. Okuyama, K. Sato and M.H. Shahid, Sasakian statistical
manifolds, J. Geom. Phys. 117, 179–186, 2017.
- [12] S. Ianuş, Statistical manifolds and tangent bundles, Sci. Bull. Univ. Politechnica of
Bucharest Ser. D, 56, 29–34, 1994.
- [13] S. Lauritzen, Statistical manifolds, in: Differential Geometry in Statistical Inference,
IMS Lecture Notes 10, 163–216, 1987.
- [14] H. Matsuzoe and J.I. Inoguchi, Statistical structures on tangent bundles, APPS. Appl.
Sci. 5 (1), 55–57, 2003.
- [15] J.M. Oller and C.N. Caudras, Rao’s distance for negative multinomial distributions,
Sankhya A, 47 (1), 75–83, 1985.
- [16] C.R. Rao, Information and accuracy attainable in estimation of statistical parameters,
Bull. Cal. Math. Soc. 37, 81–91, 1945.
- [17] K. Sun and S. Marchand-Maillet, An information geometry of statistical manifold
learning, Proceedings of the 31st International Conference on Machine Learning
(ICML-14), 1–9, 2014.
- [18] K. Yano and S. Ishihara, Tangent and cotangent bundles, Marcel Dekker Inc., New
York, 1973.