On mutual information, likelihood-ratios and estimation error for the additive Gaussian channel

This paper considers the model of an arbitrary distributed signal x observed through an added independent white Gaussian noise w, y=x+w. New relations between the minimal mean square error of the non-causal estimator and the likelihood ratio between y and \omega are derived. This is followed by an extended version of a recently derived relation between the mutual information I(x;y) and the minimal mean square error. These results are applied to derive infinite dimensional versions of the Fisher information and the de Bruijn identity. The derivation of the results is based on the Malliavin calculus.Comment: 21 pages, to appear in the IEEE Transactions on Information Theor

Rotations and Tangent Processes on Wiener Space

The paper considers (a) Representations of measure preserving transformations (``rotations'') on Wiener space, and (b) The stochastic calculus of variations induced by parameterized rotations \{T_\theta w, 0 \le \theta \le \eps\}: ``Directional derivatives'' $(dF(T_\theta w)/d \theta)_{\theta=0}$, ``vector fields'' or ``tangent processes'' $(dT_\theta w /d\theta)_{\theta=0}$ and flows of rotations.Comment: 29 page