Gaussian regression and optimal finite dimensional linear models

The problem of regression under Gaussian assumptions is treated generally. The relationship between Bayesian prediction, regularization and smoothing is elucidated. The ideal regression is the posterior mean and its computation scales as O(n³), where n is the sample size. We show that the optimal m-dimensional linear model under a given prior is spanned by the first m eigenfunctions of a covariance operator, which is a trace-class operator. This is an infinite dimensional analogue of principal component analysis. The importance of Hilbert space methods to practical statistics is also discussed.

Theory of optimal power budget in quasi-linear dispersion-managed fibre links

A theory of an optimal distribution of the gain of in-line amplifiers in dispersion-managed transmission systems is developed. As an example of the application of the general method, a design of the line with periodically imbalanced in-line amplification is proposed.