SAMUEL BOWONG∗,†,§ and JURGEN KURTHS†,‡,¶ ∗Laboratory of Applied Mathematics, Department of Mathematics and Computer Science, Faculty of Science, University of Douala, P. O. Box 24157 Douala, Cameroon †Postdam Institute for Climate Impact Research (PIK ), Telegraphenberg A 31, 14412 Potsdam, Germany ‡Department of Physics, Humboldt Universitat zu Berlin, 12489 Berlin, Germany §[email protected]...

There has been a recent surge of interest in estimation theory based on very simple noise descriptions ; for example, the absolute value of a noise sample is simply bounded. To date this line of work has not been critically compared to pre-existing work on stochastic estimation theory which uses more complicated noise descriptions. The present paper attempts to redress this gap by examining the...

The paper is dedicated to problem of unknown frequency identification of an unmeasured biased harmonic disturbance ) sin( ) ( 0 φ ω σ σ + + = t t y affecting a nonlinear system. Unlike known analogues, this approach allows to regulate time of estimation of unknown frequency ω .

In this paper the problem of parameter estimation of an input – output system is discussed. It is assumed that the system is described by the relation known with accuracy to some parameters. The possible noisy observations of system are described. The estimation algorithm based on maximum likelihood method is proposed. The presented approach is illustrated by analytical examples.

This paper introduces the Mixed Birnbaum model with collateral information. A simulation study is conducted to evaluate parameter estimation for this model. More specifically, the gain of incorporating collateral information into the model is investigated. The results show that the standard errors as well as latent class assignment can benefit substantially from incorporating external variables...

3 Models 5 3.1 Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Joint determination of anaphoricity and coreference . . . . . . . . . . . . . . 6 3.3 Unsupervised training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3.1 Selecting the number of clusters . . . . . . . . . . . . . . . . . . . . . 7 3.3.2 Non-parametric training . . . ....