Brownian motion: the hyperbolic number setting
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arXiv
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| Format: | Preprint |
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2026
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| author | Alpay, Daniel Cho, Ilwoo Mayats-Alpay, Liora |
| author_facet | Alpay, Daniel Cho, Ilwoo Mayats-Alpay, Liora |
| contents | The purpose of this paper is to define normal Gaussian variables in the setting of hyperbolic probabilities, and introduce an associated Brownian motion, when both the index and the values of the process lie in the real algebra $\mathbb{H}$ of hyperbolic numbers.
In Hida's white noise space, we construct two probability measures (say $P_1$ and $P_2$), and associate to them two families of $N(0,1)$ variables $(Z_n)_{n\in\mathbb N_0}$ (independent with respect to $P_1$) and $(W_n)_{n\in\mathbb N_0}$ (independent with respect to $P_2$). An important feature is that the $Z_n$ and $W_m$ need not be mutually independent either with respect to $P_1$ or $P_2$. An hyperbolic normal Gaussian variable is constructed (in non-degenerate cases) from two classical Gaussian variables and the hyperbolic Brownian motion is, in general, composed from two copies of the classical Brownian motion. Using the associated Gelfand triples we also compute the derivative of the hyperbolic Brownian motion as a stochastic distribution. The argument extends to the $\mathbb{H}$-valued fractional Brownian motion, and more generally to a wide family of $\mathbb{H}$-valued stationary-increment second order processes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_30616 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Brownian motion: the hyperbolic number setting Alpay, Daniel Cho, Ilwoo Mayats-Alpay, Liora Probability Functional Analysis 20G20, 46S10, 47S10 The purpose of this paper is to define normal Gaussian variables in the setting of hyperbolic probabilities, and introduce an associated Brownian motion, when both the index and the values of the process lie in the real algebra $\mathbb{H}$ of hyperbolic numbers. In Hida's white noise space, we construct two probability measures (say $P_1$ and $P_2$), and associate to them two families of $N(0,1)$ variables $(Z_n)_{n\in\mathbb N_0}$ (independent with respect to $P_1$) and $(W_n)_{n\in\mathbb N_0}$ (independent with respect to $P_2$). An important feature is that the $Z_n$ and $W_m$ need not be mutually independent either with respect to $P_1$ or $P_2$. An hyperbolic normal Gaussian variable is constructed (in non-degenerate cases) from two classical Gaussian variables and the hyperbolic Brownian motion is, in general, composed from two copies of the classical Brownian motion. Using the associated Gelfand triples we also compute the derivative of the hyperbolic Brownian motion as a stochastic distribution. The argument extends to the $\mathbb{H}$-valued fractional Brownian motion, and more generally to a wide family of $\mathbb{H}$-valued stationary-increment second order processes. |
| title | Brownian motion: the hyperbolic number setting |
| topic | Probability Functional Analysis 20G20, 46S10, 47S10 |
| url | https://arxiv.org/abs/2605.30616 |