Journals / Hacettepe Journal of Mathematics and Statistics / 2026 / Volume: 54 Issue: 2

$f$-statistical connections and Miao-Tam statistical manifolds

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Publication Date
2025-04-28
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

Finally we introduce Miao-Tam statistical manifolds and study properties of them.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We introduce $f$-statistical connections as a family of statistical connections and study some geometric objects associated to these connections such as divergence, curvature and Ricci tensors, Hessian and Laplacian operators.
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Key Findings & Data:We construct examples of $f$-statistical connections and study the introducing concepts on them.
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Conclusion & Impact:Finally we introduce Miao-Tam statistical manifolds and study properties of them.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:f-statistical connction · Hessian operator · Laplacian operator

Abstract

We introduce $f$-statistical connections as a family of statistical connections and study some geometric objects associated to these connections such as divergence, curvature and Ricci tensors, Hessian and Laplacian operators. We construct examples of $f$-statistical connections and study the introducing concepts on them. Finally we introduce Miao-Tam statistical manifolds and study properties of them.

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Cite This Article

Peyghan, E., Nourmohammadifar, L., Ali, A. (2025). $f$-statistical connections and Miao-Tam statistical manifolds. Hacettepe Journal of Mathematics and Statistics, Volume: 54 Issue: 2. https://doi.org/10.15672/hujms.1414765

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References (15)

  1. S. Amari, Information geometry of the EM and em algorithms for neural networks, Neural Networks 8 (9), 1379–1408, 1995.
  2. K. Arwini and C.T.J. Dodson, Neighbourhoods of independence and associated geometry in manifolds of bivariate Gaussian and Freund distributions, Open Math. 5 (1), 50–83, 2007.
  3. B. Balcerzak, Linear connection and secondary characteristic classes of Lie algebroids, Monographs of Lodz University of Technology, 2021. Doi:10.34658/9788366741287.
  4. H. Baltazar and A. Da Silva, On static manifolds and related critical spaces with cyclic parallel Ricci tensor, Advances in Geometry 21 (3), 443–450, 2021.
  5. A. Barros, R. Diógenes and E. Ribeiro Jr, Bach-Flat Critical Metrics of the Volume Functional on 4-Dimensional Manifolds with Boundary, J. Geom. Anal. 25, 2698– 2715, 2015.
  6. M. Belkin, P. Niyogi and V. Sindhwani, Manifold regularization: a geometric framework for learning from labeled and unlabeled examples, Journal of Machine Learning Research 7, 2399–2434, 2006.
  7. O. Calin and C. Udrişte, Geometric Modeling in Probability and Statistics, Springer, Cham, Switzerland, 2014.
  8. A. Caticha, Geometry from information geometry, arxiv.org/abs/1512.09076v1.
  9. A. Caticha, The information geometry of space and time, Proceedings 33 (1), 15, 2019.
  10. R. A. Fisher, On the mathematical foundations of theoretical statistics, Phil. Trans. Roy. Soc. London. 222, 309–368, 1922.
  11. S. Kobayashi and Y. Ohno, On a constant curvature statistical manifold, Inf. Geom. 5 (1), 31-46, 2022.
  12. P. Miao and L.F. Tam, On the volume functional of compact manifolds with boundary with constant scalar curvature, Calc. Var. PDE. 36, 141–171, 2009.
  13. P. Miao and L.F. Tam, Einstein and conformally flat critical metrics of the volume functional, Trans. Amer. Math. Soc. 363, 2907–2937, 2011.
  14. B. Opozda, Bochners technique for statistical structures, Ann. Global Anal. Geom. 48, 357–395, 2015.
  15. 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.