A note on mean square maximum and minimum principles in dynamic linear viscoelasticity

Notes on Linear Minimum Mean Square Error Estimators

Some connections between linear minimum mean square error estimators, maximum output SNR filters and the least square solutions are presented. The notes have been prepared to be distributed with EE 503 (METU, Electrical Engin.) lecture notes. 1 Linear Minimum Mean Square Error Estimators The following signal model is assumed: r = Hs + v (1) Here r is a N × 1 column vector denoting the observati...

Variational Principles in the Linear Dynamic Theory of Viscoelasticity

A note on finding minimum mean cycle

In a directed graph with edge weights, the mean weight of a directed cycle is the weight of its edges divided by their number. The minimum cycle mean of the graph is the minimum mean weight of a cycle. Karp gave a characterization of minimum cycle mean and an O(nm) algorithm to compute it, where n is the number of vertices and m is the number of edges. However, an algorithm he suggested for ide...

Note on a new computational data smoothing procedure suggested by minimum mean square error estimation

Minimum mean square estimation and neural networks

Neural networks for estimation, such as the multilayer perceptron (MLP) and functional link net (FLN), are shown to approximate the minimum mean square estimator rather than the maximum likelihood estimator or others. Cramer-Rao maximum a posteriori lower bounds on estimation error can therefore be used to approximately bound network training error, when a statistical signal model is available ...