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The main purpose of this paper of the paper is an explicite construction of generalized Gaussian process with function $t_b(V)=b^{H(V)}$, where $H(V)=n-h(V)$, $h(V)$ is the number of singletons in a pair-partition $V \in \st{P}_2(2n)$. This…

Probability · Mathematics 2013-01-14 Marek Bozejko , Wojciech Bozejko

The concept of scaled quadratic variation was originally introduced by E. Gladyshev in 1961 in the context of Gaussian processes, where it was defined as the limit of the covariance of the underlying Gaussian process. In this paper, we…

Probability · Mathematics 2025-07-17 Suprio Bhar , Purba Das , Barun Sarkar

Gaussian processes occupy one of the leading places in modern statistics and probability theory due to their importance and a wealth of strong results. The common use of Gaussian processes is in connection with problems related to…

Statistics Theory · Mathematics 2023-02-01 Zexun Chen , Jun Fan , Kuo Wang

Motivated by applications, we introduce a general and new framework for operator valued positive definite kernels. We further give applications both to operator theory and to stochastic processes. The first one yields several dilation…

Functional Analysis · Mathematics 2024-07-31 Palle E. T. Jorgensen , James Tian

We derive a precise link between series expansions of Gaussian random vectors in a Banach space and Parseval frames in their reproducing kernel Hilbert space. The results are applied to pathwise continuous Gaussian processes and a new…

Probability · Mathematics 2013-04-03 Harald Luschgy , Gilles Pagès

We present three new identities in law for quadratic functionals of conditioned bivariate Gaussian processes. In particular, our results provide a two-parameter generalization of a celebrated identity in law, involving the path variance of…

Probability · Mathematics 2007-05-23 Giovanni Peccati , Marc Yor

Gaussian couplings of partial sum processes are derived for the high-dimensional regime $d=o(n^{1/3})$. The coupling is derived for sums of independent random vectors and subsequently extended to nonstationary time series. Our inequalities…

Probability · Mathematics 2022-03-08 Fabian Mies , Ansgar Steland

We prove a Galois-type correspondence between compositions of purely inseparable field extensions (including infinite ones) and subalgebras of differential operators. This correspondence can be utilized to establish a connection between…

Algebraic Geometry · Mathematics 2023-07-24 Przemyslaw Grabowski

A very explicit analytic formula of the separability criterion of two-party Gaussian systems is given. This formula is compared to the past formulation of the separability criterion of continuous variables two-party Gaussian systems.

Quantum Physics · Physics 2015-06-03 Kazuo Fujikawa

Let $\{X_i(t),t\ge0\}, 1\le i\le n$ be mutually independent centered Gaussian processes with almost surely continuous sample paths. We derive the exact asymptotics of $$ P\left(\exists_{t \in [0,T]} \forall_{i=1 ... n} X_i(t)> u \right) $$…

Probability · Mathematics 2015-05-26 Krzysztof Dȩbicki , Enkelejd Hashorva , Lanpeng Ji , Kamil Tabiś

This paper investigates extreme value theory for processes obtained by applying transformations to stationary Gaussian processes, also called subordinated Gaussian processes. The main contributions are as follows. First, we refine the…

Probability · Mathematics 2026-05-29 Shuyang Bai , Marie-Christine Duker

We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of local sequential design schemes that dynamically define the…

Methodology · Statistics 2014-10-13 Robert B. Gramacy , Daniel W. Apley

Parameterized quantum circuits are the core of new technologies such as variational quantum algorithms and quantum machine learning, which makes studying its properties a valuable task. We implement parameterized circuits with definite and…

Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes. While this model family promises flexible predictive distributions, exact inference is not tractable.…

Machine Learning · Statistics 2020-10-23 Jakob Lindinger , David Reeb , Christoph Lippert , Barbara Rakitsch

Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty. These problems typically exist as parts of…

We provide a sufficient condition for the continuity of real valued permanental processes. When applied to the subclass of permanental processes which consists of squares of Gaussian processes, we obtain the sufficient condition for…

Probability · Mathematics 2013-03-18 Michael B. Marcus , Jay Rosen

We establish properties of a new type of fractal which has partial self similarity at all scales. For any collection of iterated functions systems with an associated probability distribution and any positive integer V there is a…

Dynamical Systems · Mathematics 2008-02-04 Michael Barnsley , John E. Hutchinson , Örjan Stenflo

We study the persistence probability for some two-sided discrete-time Gaussian sequences that are discrete-time analogs of fractional Brownian motion and integrated fractional Brownian motion, respectively. Our results extend the…

Probability · Mathematics 2018-02-14 Frank Aurzada , Micha Buck

Cohen and Kontorovich (COLT 2023) initiated the study of what we call here the Binomial Empirical Process: the maximal absolute value of a sequence of inhomogeneous normalized and centered binomials. They almost fully analyzed the case…

Probability · Mathematics 2024-02-13 Moïse Blanchard , Doron Cohen , Aryeh Kontorovich

We study distributions of random vectors whose components are second order polynomials in Gaussian random variables. Assuming that the law of such a vector is not absolutely continuous with respect to Lebesgue measure, we derive some…

Probability · Mathematics 2013-05-28 Vladimir I. Bogachev , Egor D. Kosov , Ivan Nourdin , Guillaume Poly