White noise approach to Gaussian random fields

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود 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

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 filtering of a random signal in Gaussian white noise

0 φi(t) dw(t) are i.i.d. N (0, 1) random variables. Thus, if we set ε = n −1/2, then the problem of estimating an unknown signal from observations (1) is equivalent to the problem of estimating a signal in the Gaussian white noise model (2) (see [1,2]). That is why, sometimes we call the vector θ in model (1) a signal. The Gaussian white noise model (2) for ε = n−1/2 is a good approximation for...

متن کامل

Detecting Markov Random Fields Hidden in White Noise

Motivated by change point problems in time series and the detection of textured objects in images, we consider the problem of detecting a piece of a Gaussian Markov random field hidden in white Gaussian noise. We derive minimax lower bounds and propose near-optimal tests.

متن کامل

Skew-Gaussian Random Fields

Skewness is often present in a wide range of spatial prediction problems, and modeling it in the spatial context remains a challenging problem. In this study a skew-Gaussian random field is considered. The skew-Gaussian random field is constructed by using the multivariate closed skew-normal distribution, which is a generalization of the traditional normal distribution. We present an Metropolis...

متن کامل

Gaussian Process Random Fields

Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a simple, tractable, and paralle...

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: Nagoya Mathematical Journal

سال: 1990

ISSN: 0027-7630,2152-6842

DOI: 10.1017/s0027763000003135