Nonparametric Bernstein–von Mises theorems in Gaussian white noise

On the Bernstein-von Mises phenomenon in the Gaussian white noise model

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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 .

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...

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 ...

Bernstein von Mises Theorems for Gaussian Regression with increasing number of regressors