Regularity of Gaussian white noise on the d-dimensional torus

نویسندگان

چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Generalized free Gaussian white noise

Based on an adequate new Gel’fand triple, we construct the infinite dimensional free Gaussian white noise measure μ using the Bochner-Minlos theorem. Next, we give the chaos decomposition of an L2 space with respect to the measure μ .

متن کامل

NOISE INDUCED DISSIPATION IN LEBESGUE-MEASURE PRESERVING MAPS ON d−DIMENSIONAL TORUS

We consider dissipative systems resulting from the Gaussian and alpha-stable noise perturbations of measure-preserving maps on the d dimensional torus. We study the dissipation time scale and its physical implications as the noise level ε vanishes. We show that nonergodic maps give rise to an O(1/ε) dissipation time whereas ergodic toral automorphisms, including cat maps and their d-dimensional...

متن کامل

Stochastic parallel transport on the d-dimensional Torus

In Ref. 2 V. Arnold has shown that the Euler flow can be identified with a geodesic on the group G of volume preserving diffeomorphisms with respect to the L metric. Following this approach, the geometry of G plays a fundamental role in hydrodynamics and is important for instance in the study of the stability of the fluids motion. It has been developed by many authors, one of the first being Re...

متن کامل

White Noise Representation of Gaussian Random Fields

We obtain a representation theorem for Banach space valued Gaussian random variables as integrals against a white noise. As a corollary we obtain necessary and sufficient conditions for the existence of a white noise representation for a Gaussian random field indexed by a measure space. We then show how existing theory for integration with respect to Gaussian processes indexed by [0, 1] can be ...

متن کامل

Adaptive AR modeling in white Gaussian noise

Autoregressive (AR) modeling is widely used in signal processing. The coefficients of an AR model can be easily obtained with a least mean square (LMS) prediction error filter. However, it is known that this filter gives a biased solution when the input signal is corrupted by white Gaussian noise. Treichler suggested the -LMS algorithm to remedy this problem and proved that the mean weight vect...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Banach Center Publications

سال: 2011

ISSN: 0137-6934,1730-6299

DOI: 10.4064/bc95-0-24