Abstract
This paper aims to develop a general theory of quaternion Kahlerian statistical manifolds and to study quaternion CR-statistical submanifolds in such ambient manifolds. It extends the existing theories of quaternion submanifolds and totally real submanifolds. Additionally, the work examines quaternion Kahlerian statistical submersions, including illustrative examples. The exploration also includes an analysis of the total space and fibers under certain conditions with example(s) in support. Moreover, Chen–Ricci inequality on the vertical distribution is derived for quaternion Kahlerian statistical submersions from quaternion Kahlerian statistical manifolds.
Keywords:
statistical manifolds; quaternion Kahlerian statistical manifolds; Lagrangian submanifolds; quaternion CR-statistical submanifolds; statistical submersions; quaternion Kahlerian statistical submersions; Chen–Ricci inequality MSC:
53C40; 53C12
1. Introduction
A quaternion (or quaternionic) Kahlerian manifold is a Riemannian manifold whose holonomy group is contained within , the quaternionic unitary group. This condition ensures the existence of a quaternionic structure on the tangent bundle, characterized by a rank-3 subbundle of the endomorphism bundle that is locally spanned by almost complex structures satisfying the quaternionic relations. Such manifolds naturally arise in mathematical physics, particularly in the study of supersymmetry and string theory, as well as in representation theory and twistor theory.
Unlike their Kahlerian counterparts, quaternion Kahlerian manifolds generally do not admit a global complex structure. However, they possess a rich set of geometric properties, including a hypercomplex structure in special cases and a distinguished 4-form known as the quaternionic Kahler form. These features enable profound links between differential geometry, topology, and algebraic geometry.
Quaternion Kahlerian manifolds can be classified into two main categories: those with positive scalar curvature, which are compact and often related to symmetric spaces; and those with non-positive scalar curvature, which frequently appear in the study of moduli spaces and special holonomy. In addition, the subclass of hyperkähler manifolds, where the holonomy reduces further to , has garnered particular attention due to their rich integrable structures and applications in algebraic geometry.
The interplay between geometry and statistics has given rise to the field of information geometry, where statistical manifolds serve as a central object of study. A statistical manifold is a Riemannian manifold equipped with a pair of dual affine connections, enabling the geometric interpretation of probabilistic and information-theoretic concepts. Statistical manifolds represent a significant topic with extensive applications across various fields, including machine learning, image analysis, neural networks, physics, general relativity, and control systems, among others [1,2]. These manifolds arise from statistical models, where the points of the Riemannian manifold correspond to probability distributions. In recent years, the study of statistical manifolds has gained considerable attention, leading to numerous remarkable findings by researchers in the field [3,4,5,6]. When extended to a quaternionic setting, this leads to the theory of quaternion Kahlerian statistical manifolds, which integrate quaternionic geometry with statistical structures to explore deeper symmetries and interactions.
On the other hand, submersions play a fundamental role in differential geometry by establishing structured relationships between manifolds of differing dimensions. A submersion is a smooth map between manifolds that has surjective differential maps at every point. Among these, Riemannian submersions and statistical submersions stand out for their geometric and statistical significance, respectively. Building on the concept of Riemannian submersions, a statistical submersion is studied in [7]. Statistical submersions provide a unified framework for understanding the mappings between statistical manifolds, making them a cornerstone of modern research in differential geometry, information theory, and applied statistics. Their theoretical richness and practical applicability continue to inspire new avenues of study.
In this article, we begin by recalling some fundamental and essential notions. Consistent with the frameworks of holomorphic, Sasakian, and Kenmotsu statistical manifolds, we propose the statistical analog of a quaternion Kahlerian manifold, referred to as a quaternion Kahlerian statistical manifold, with an example. Naturally, this leads to the exploration of submanifold theory in the context of statistical manifolds. We examine key results related to statistical immersions and quaternion CR-statistical submanifolds in the same ambient manifold. Also, it is an interesting and important problem to study the quaternion Kahlerian statistical structure of constant Q-sectional curvature c. Furthermore, we derive several findings on quaternion Kahlerian statistical submersions, illustrated with example(s), and specifically investigate the relationship between the statistical curvature tensors of the base and target manifolds under such submersions.
2. Preliminaries
Definition 1.
Let be a Riemannian manifold of dimension r and D a torsion-free linear connection on M. Then, the pair is called a statistical structure on M if D is compatible with g, that is, is symmetric. A triple is known as a statistical manifold [3].
Now, let be an affine connection on such that for all .
It follows that is symmetric and is torsion-free. Note that is known as the dual statistical manifold of and that is the dual connection of D.
The concept of dual connections, named because , was first introduced by S. Amari in his pioneering work. It has since found applications in diverse fields, such as information theory, statistical physics, and neural networks. We also write the Levi–Civita connection on M as .
Let N be a submanifold of M and g be the induced metric on N. Then, is a statistical submanifold in a statistical manifold if is the induced statistical structure on N.
A pair becomes a statistical structure if and only if the difference tensor satisfies the following conditions:
- ;
- .
Furthermore, we have the relation .
Remark 1.
The curvature tensor field of D is indicated by and is indicated by for , and for [6,8].
- 1.
- The statistical curvature tensor field of is defined asfor .
- 2.
- Let an orthonormal basis of be . For a plane of , the statistical sectional curvature of is defined byfor . Note that the sectional curvature of g is obtained using instead of .
- 3.
- The statistical scalar curvature of is defined asThe scalar curvature of g is given by using instead of .
Furuhata et al. [9] proposed a new notion of sectional curvature for statistical manifolds, distinct from earlier definitions that use either the statistical curvature tensor field (as defined above) or the K-curvature tensor field .
Definition 2.
Let be a statistical manifold. Set as
where .
- 1.
- For a plane , the sectional curvature of is given by
- 2.
- The scalar curvature is
3. Quaternion Kahlerian Statistical Manifolds
Let M be a smooth manifold with a rank-3 subbundle of type on such that the sections of admit a local basis satisfying
Then, is called an almost quaternion structure on M. A couple is known as an almost quaternion manifold. An almost quaternion manifold M is of dimension provided .
On an almost quaternion manifold , a Riemannian metric g exists such that , for any cross-section . Consequently, is referred to as an almost quaternion metric manifold.
Let be a canonical local basis of of . Since each of , , and is almost Hermitian with respect to g, gives
where are local 2-forms.
Definition 3.
An almost quaternion metric manifold M satisfies
for any vector field . Then, M is called a quaternion Kahlerian manifold.
Following the framework used for holomorphic, Sasakian, Kenmotsu, nearly Sasakian, and nearly Kähler statistical manifolds (see [10,11,12,13]), we now introduce the statistical counterpart of a quaternion Kahlerian manifold, referred to as the quaternion Kahlerian statistical manifold.
Definition 4.
A triple is a quaternion Kahlerian statistical structure on a smooth manifold M if the following is the case:
- 1.
- is a statistical structure on M;
- 2.
- is an almost quaternion metric structure on M;
- 3.
- The formulas , , hold for any .
A quaternion Kahlerian statistical manifold is a manifold equipped with a quaternion Kahlerian statistical structure.
Remark 2.
It is worth noting that is a quaternion Kahlerian statistical manifold, so is . Also, note that our Definition 3 is equivalent to Definition 4.
Example 1.
Let be an almost quaternion metric structure on M. Consider a chosen vector field, and we construct
for any . Then, for , K satisfies the following:
- 1.
- ;
- 2.
- ;
- 3.
- .
Thus, we have a quaternion Kahlerian statistical structure on M.
Example 2.
Consider , a semi-Riemannian manifold with the local coordinate system and
which admits the following quaternion Kahlerian structure , such that
By Example 1, we can construct a quaternion Kahlerian statistical structure on .
We know that the sectional curvature for the statistical structure is defined using the tensor field rather than R, as it does not possess the necessary symmetries characteristic of the curvature tensor field to a Levi–Civita connection. So, we have the following:
Let be a quaternion Kahlerian statistical manifold, and let . Suppose E is a non-null vector field on M. Then, is a 4-dimensional subspace of at each , spanned by . The sectional curvature of a 2-plane in is referred to as the Q-sectional curvature. The quaternion Kahlerian statistical structure is defined as having a constant Q-sectional curvature c if the curvature tensor for D and satisfies
Proposition 1.
On quaternion Kahlerian statistical manifold , we have
- 1.
- ;
- 2.
- ;
- 3.
- ;
for .
Theorem 1.
Let be a statistical structure and a quaternion Kahlerian structure on M; is a quaternion Kahlerian statistical structure if and only if the following formula holds:
for , where the indices are taken from modulo 3.
4. Statistical Immersions
In this section, we present the foundational concepts of statistical submanifolds in a quaternion Kahelrian statistical manifold, excluding aspects of information geometry.
For any , and , , the Gauss and Weingarten formulas are, respectively, defined by [14]
Here, () is a symmetric and bilinear tensor, referred to as the embedding curvature tensors N in M for D, while () represents the induced dual connection on N (). Moreover, the dual connections and of the vector bundles and are called dual normal connections.
Also, we know
Definition 5.
Let be a statistical submanifold of .
- 1.
- If , then N is doubly minimal.
- 2.
- If and , then N is doubly totally umbilical such that () is the mean curvature vector of N for D () in M.
- 3.
- If , then N is doubly totally geodesic.
We recall from [4] the concept of statistical immersion , where and for . Then, the Gauss formula is
Therefore, in our settings, we put the statistical structure on N induced by h from . Then, is a quaternion Kahlerian statistical structure on N, where denotes a rank-3 subbundle of type of such that the sections of admit a local basis satisfying (4). Thus, we have the following:
Theorem 2.
Let be a quaternion Kahelrian statistical manifold and a quaternion isometric immersion. Then, the following holds:
- 1.
- h is doubly minimal.
- 2.
- holds for .
Proof.
We use Theorem 1 for ,
But we know holds, so
On the other hand, we have
It is easy to see that . Therefore, h is doubly minimal. □
We, respectively, symbolize the Riemannian curvature tensors of () by R (). Then, the corresponding Gauss equations, for conjugate affine connection, are given by [14]
and
where
and
Thus, we have the Gauss formula for both affine connections:
where .
Also, we have
where denotes the Riemannian curvature tensor for Levi–Civita connection on N.
Proposition 2.
Let be a quaternion Kahlerian statistical manifold and N be a quaternion submanifold of M. Then, the following hold:
- 1.
- is a quaternion Kahlerian statistical manifold.
- 2.
- ,
for .
Proof.
We use Theorem 1
On comparing normal components, we obtain . Similarly, we have . □
5. Quaternion CR-Statistical Submanifolds
In this section, we give the definition of quaternion CR-statistical submanifolds and generalize classical theorems to our setting.
Definition 6.
A submanifold N in a quaternion Kahelrian statistical manifold is called a quaternion CR-statistical submanifold if the following hold:
- 1.
- is a statistical submanifold in ;
- 2.
- There exists a differentiable distribution , satisfying the following:
- (a)
- is quaternion, that is, ;
- (b)
- the orthogonal complementary distribution is totally real, that is, .
Definition 7.
A statistical submanifold N in a quaternion Kahelrian statistical manifold is called a quaternion CR-statistical submanifold if the differentiable distribution , exists and satisfies the following:
- 1.
- is quaternion, that is, ;
- 2.
- The orthogonal complementary distribution is totally real, that is, .
Moreover, the quaternion CR-statistical submanifold N in has the following characteristics:
- It reduces to a quaternion submanifold (totally real submanifold) if .
- It reduces to a generic submanifold if and .
- It reduces to a Lagrangian submanifold if and .
- It is called proper if N is neither a quaternion submanifold (that is, ) nor a totally real submanifold (that is, ).
Let N be a submanifold of a quaternion Kahlerian statistical manifold . Then, we have for any the following decomposition:
. Here, () denote the tangential component (normal component) of . Similarly, for any , we write
where and are the tangential and the normal parts of , respectively.
Consequently, we have the following decomposition of the tangent bundle as a direct sum of vector bundles:
where denotes a subbundle of defined by , .
Concerning the integrability of the distributions and for a quaternion CR-statistical submanifold:
Theorem 3.
On a quaternion CR-statistical submanifold of a quaternion Kahlerian statistical manifold , we have that is integrable if and only if , for .
Proof.
It is easy to derive
□
Since, is completely integrable. Therefore, we can have the following result:
Lemma 1.
On a quaternion CR-statistical submanifold of a quaternion Kahlerian statistical manifold , we have , (or ), for .
Proof.
It is easy to obtain
□
Lemma 2.
On a Lagrangian submanifold of a quaternion Kahlerian statistical manifold , we have that , for .
Proof.
For and , we have
Considering the normal parts of the above equation
We put in the above relation and obtain
□
Theorem 4.
For a Lagrangian submanifold in a quaternion Kahlerian statistical manifold of constant Q-sectional curvature c, if we assume that for , then N is of constant sectional curvature .
Proof.
By using (12) and Lemmas 1 and 2, we arrive at the desired result. □
Theorem 5.
Let be a Lagrangian submanifold of a quaternion Kahlerian statistical manifold of constant Q-sectional curvature c. If we assume that , then N is of constant -sectional curvature .
6. Quaternion Kahlerian Statistical Submersions
The concept of statistical submersion between statistical manifolds was first proposed by N. Abe and K. Hasegawa [7]. Their work expanded upon foundational results established by B. O’Neill [15] regarding Riemannian submersions and geodesics. Later, statistical submersions with different statistical structures were studied (see [16]), and obtained several geometric properties (see [17,18,19,20]).
Consider two statistical manifolds, and , with and . Assume that . Then, a statistical submersion is a smooth mapping of M onto if these conditions hold [7,21]:
- has maximal rank;
- The differential preserves the length of horizontal vectors;
- ;
where is related to U and V on while and are basic vector fields on M.
The -dimensional statistical submanifold , is known as a fiber , equipped with the induced metric G. We will denote throughout. The affine connections on are represented by and .
A vector field on M is classified as horizontal if it is always orthogonal to the fibers, and as vertical if it is always tangent to the fibers. For each , the vertical and horizontal subspaces in the tangent space of the total space M are denoted by and , respectively.
The tangent bundle can be decomposed as
where and are the horizontal and vertical distributions, respectively. The projection mappings onto these distributions are denoted by and .
The geometry of statistical submersions is distinguished by the tensors and , O’Neil’s tensors, of type (see [15]), along with their dual counterparts, and , which are derived by replacing D with , as described in [17].
Remark 3.
The following points are important to note:
- 1.
- and .
- 2.
- and are symmetric for vertical vector fields.
- 3.
- For vertical vector fields coincides with the second fundamental form of the immersion of the fiber submanifolds.
- 4.
- (or ) if and only if (or ).
- 5.
- For horizontal vectors is symmetric if and only if is integrable with respect to D.
Denote by and the curvature tensor of each fiber with respect to and that on of . Then, from [17], we have the following equations:
for all vertical vector fields , , , and on .
for all horizontal vector fields , , , and on .
In [20], A.D. Vilcu and G.E. Vilcu introduced the concept of a quaternionic Kähler-like statistical manifold by defining a dualistic pair of tensor fields, , and subsequently explored the key properties of quaternionic Kähler-like statistical submersions within this framework. In contrast, we consider a different notion of quaternion Kahlerian statistical manifolds, as discussed in the preceding sections. In this section, we define and establish some fundamental properties of quaternion Kahlerian statistical submersions.
Definition 8.
A quaternion Kahlerian statistical submersion is the statistical submersion , where is a quaternion Kahlerian statistical manifold and is a statistical manifold.
Thus, we have the following:
Theorem 6.
For a quaternion Kahlerian statistical submersion . If is a -invariant submanifold of M, then ι is with isometric fiber, that is, .
Theorem 7.
For a quaternion Kahlerian statistical submersion . If is a -invariant submanifold of M, then the horizontal distribution is completely integrable, that is, .
Proof.
From Theorem 1, we obtain the following:
for the vertical vector field and the horizontal vector fields and on M. Therefore, we can compute that
In particular, we have
On the other hand, using the relation , we derive
which can be simplified further as
Thus, , and similarly, we conclude that . □
Example 3.
Let be a statistical manifold with local coordinate system , and flat connection. We consider the quaternion Kahlerian statistical manifold from Example 2 and define the quaternion Kahlerian statistical submersion as the projection mapping . Then, we find . And here each fiber is a totally geodesic semi-Riemannian submanifold of .
For a vertical vector field and horizontal vector field , we put
and
where and are horizontal parts and and are vertical parts.
We consider the curvature with respect to D of the total space satisfies
for , where . Then, from (16), (22) and (23), we find
From Equation (24) and , we find that or
If we consider the equation
it implies that for . Consequently, it follows that if for . Hence, we conclude the following:
7. Chen–Ricci Inequality
Several researchers have established the Chen–Ricci inequality for specific types of submanifolds in various ambient spaces. Also, particular cases of these inequalities have been demonstrated in statistical contexts (see [22,23,24]). This section focuses on deriving the Chen–Ricci inequality for the vertical distribution in quaternion Kahlerian statistical submersions from quaternion Kahlerian statistical manifolds.
Let be a quaternion Kahlerian statistical submersion from a -dimensional quaternion Kahlerian statistical manifold of constant Q-sectional curvature c onto an n-dimensional statistical manifold . Then, we suppose the orthonormal bases and on the vertical space and the horizontal space, respectively. Then, the 4rean curvature vectors , , and with respect to D, , and are given, respectively, by
Here, the equality holds if and only if , , , , and , .
Thus, we have
By the Gauss equation for the Levi–Civita connection, we have
Further, we rewrite it as
Theorem 9.
Let be a quaternion Kahlerian statistical submersion from a -dimensional quaternion Kahlerian statistical manifold of constant Q-sectional curvature c onto statistical manifold . Then,
The equality holds if and only if , , and , .
Remark 4.
In future research, one can expand this study’s scope by deriving the Chen–Ricci inequality for the horizontal distribution in quaternion Kahlerian statistical submersions from quaternion Kahlerian statistical manifolds. Furthermore, it would be interesting to establish such an inequality that intricately links the vertical and horizontal distributions, unveiling deeper geometric interrelations. These advancements are expected to provide significant insights and enrich the theoretical framework of quaternion Kahlerian statistical geometry, paving the way for further exploration and applications in this domain.
Author Contributions
Conceptualization, A.N.S. and F.A.; Methodology, A.N.S. and F.A.; Software, A.N.S. and F.A.; Investigation, A.N.S. and F.A.; Writing—original draft, A.N.S. and F.A.; Writing—review & editing, A.N.S. and F.A.; Visualization, F.A.; Supervision, A.N.S.; Funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript.
Funding
University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-24-DR-3029-1).
Data Availability Statement
Data are contained within the article.
Acknowledgments
This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-24-DR-3029-1). The authors, therefore, thank the University of Jeddah for its technical and financial support. The authors are also grateful to the unknown referees for their many constructive suggestions, which improved the presentation of the paper.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Belkin, M.; Niyogi, P.; Sindhwani, V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 2006, 7, 2399–2434. [Google Scholar]
- Caticha, A. Geometry from information geometry. arXiv 2015, arXiv:1512.09076. [Google Scholar]
- Amari, S. Differential-Geometrical Methods in Statistics; Springer: New York, NY, USA, 1985. [Google Scholar]
- Furuhata, H. Toward differential geometry of statistical submanifolds. Inf. Geom. 2024, 7 (Suppl. S1), S99–S108. [Google Scholar] [CrossRef]
- Furuhata, H. Sasakian Statistical Manifolds II. In Geometric Science of Information; Nielsen, F., Barbaresco, F., Eds.; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2017; Volume 10589. [Google Scholar]
- Furuhata, H.; Hasegawa, I. Submanifold Theory in Holomorphic Statistical Manifolds. In Geometry of Cauchy-Riemann Submanifolds; Dragomir, S., Shahid, M., Al-Solamy, F., Eds.; Springer: Singapore, 2016; pp. 179–215. [Google Scholar] [CrossRef]
- Abe, N.; Hasegawa, K. An affine submersion with horizontal distribution and its applications. Differ. Geom. Appl. 2001, 14, 235–250. [Google Scholar] [CrossRef]
- Satoh, N.; Furuhata, H.; Hasegawa, I.; Nakane, T.; Okuyama, Y.; Sato, K.; Shahid, M.H.; Siddiqui, A.N. Statistical Submanifolds from a View point of the Euler Inequality. Inf. Geom. 2021, 4, 189–213. [Google Scholar] [CrossRef]
- Furuhata, H.; Hasegawa, I.; Chen, S.N. Invariants and Statistical Submanifolds. Commun. Korean Math. Soc. 2022, 37, 851–864. [Google Scholar]
- Kurose, T. Geometry of Statistical Manifolds. In Mathematics in the 21st Century; Miyaoka, R., Kotani, M., Eds.; Nihon-Hyouron-Sha: Tokyo, Japan, 2004; pp. 34–43. (In Japanese) [Google Scholar]
- Furuhata, H.; Hasegawa, I.; Okuyama, Y.; Sato, K.; Shahid, M.H. Sasakian Statistical Manifolds. J. Geom. Phys. 2017, 1117, 179–186. [Google Scholar] [CrossRef]
- Furuhata, H.; Hasegawa, I.; Okuyama, Y.; Sato, K. Kenmotsu Statistical Manifolds and Warped Product. J. Geom. 2017, 108, 1175–1191. [Google Scholar] [CrossRef]
- Uddin, S.; Peyghan, E.; Nourmohammadifar, L.; Bossly, R. On Nearly Sasakian and Nearly Kähler Statistical Manifolds. Mathematics 2023, 11, 2644. [Google Scholar] [CrossRef]
- Vos, P.W. Fundamental Equations for Statistical Submanifolds with Applications to the Bartlett Correction. Ann. Inst. Statist. Math. 1989, 41, 429–450. [Google Scholar] [CrossRef]
- O’Neill, B. The Fundamental Equations of a Submersion. Mich. Math. J. 1966, 13, 458–469. [Google Scholar] [CrossRef]
- Siddiqui, A.N.; Ahmad, K. A Study of Statistical Submersions. Tamkang J. Math. 2023, 55, 149–178. [Google Scholar] [CrossRef]
- Takano, K. Statistical Manifolds with Almost Complex Structures and Its Statistical Submersions. Tensor 2004, 65, 123–137. [Google Scholar]
- Takano, K. Statistical Manifolds with Almost Contact Structures and Its Statistical Submersions. J. Geom. 2006, 85, 171–187. [Google Scholar] [CrossRef]
- Takano, K.; Kazan, S. Statistical Submersions with Parallel Almost Complex Structures. Mediterr. J. Math. 2024, 21, 109. [Google Scholar] [CrossRef]
- Vilcu, A.D.; Vilcu, G.E. Statistical Manifolds with Almost Quaternionic Structures and Quaternionic Kähler-like Statistical Submersions. Entropy 2015, 17, 6213–6228. [Google Scholar] [CrossRef]
- Takano, K. Examples of the Statistical Submersions on the Statistical Model. Tensor 2004, 65, 170–178. [Google Scholar]
- Siddiqui, A.N.; Uddin, S.; Shahid, M.H. B.-Y. Chen’s Inequality for Kähler-like Statistical Submersions. Int. Electron. J. Geom. 2022, 15, 277–286. [Google Scholar] [CrossRef]
- Aytimur, H.; Kon, M.; Mihai, A.; Ozgur, C.; Takano, K. Chen Inequalities for Statistical Submanifolds of Kähler-Like Statistical Manifolds. Mathematics 2019, 7, 1202. [Google Scholar] [CrossRef]
- Mihai, I.; Mihai, R.-I. General Chen Inequalities for Statistical Submanifolds in Hessian Manifolds of Constant Hessian Curvature. Mathematics 2022, 10, 3061. [Google Scholar] [CrossRef]
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