This paper gives an overview of identification of linear systems. It covers the classical approach of parametric methods using Maximum Likelihood and Predicion Error Methods, as well all classical non-parametric methods through spectral analysis. It also covers very recent techniques dealing with convex formulations by regularization of FIR and ARX models, as well as new alternatives to spectra...

The process of implicitization generates an implicit representation of a curve or surface from a given parametric one. This process is potentially interesting for applications in Computer Aided Design, where the robustness and efficiency of intersection algorithm can be improved by simultaneously considering implicit and parametric representations. This paper gives an brief survey of the existi...

We extend a TCTL model-checking problem to a parametric timing analysis problem for real-time systems and develop new techniques for solving it. The algorithm we present here accepts timed transition system descriptions and parametric TCTL formulas with timing parameter variables of unknown sizes and can give back general linear equations of timing parameter variables whose solutions make the s...

This paper investigates techniques for the assessment of model error in the context of insurance risk analysis. The methodology is based on finding bounds for quantities of interest, such as loss probabilities and conditional value-at-risk, which are obtained by solving optimization problems where the variable to optimize is the model itself in a non-parametric framework. The non-parametric asp...

Statistical resampling methods have become feasible for parametric estimation, hypothesis testing, and model validation now that the computer is a ubiquitous tool for statisticians. This essay focuses on the resampling technique for parametric estimation known as the Jackknife procedure. To outline the usefulness of the method and its place in the general class of statistical resampling techniq...

In this paper, we examine the problem of predicting machine availability in desktop and enterprise computing environments. Predicting the duration that a machine will run until it restarts (availability duration) is critically useful to application scheduling and resource characterization in federated systems. We describe one parametric model fitting technique and two non-parametric prediction ...

Statistical resampling methods have become feasible for parametric estimation, hypothesis testing, and model validation now that the computer is a ubiquitous tool for statisticians. This essay focuses on the resampling technique for parametric estimation known as the Jackknife procedure. To outline the usefulness of the method and its place in the general class of statistical resampling techniq...

This paper is an initial investigation into using knowledge-based parameters in the field of statistical parametric speech synthesis (SPSS). Utilizing the types of speech parameters used in the Klatt Formant Synthesizer we present automatic techniques for deriving such parameters from a speech database and building a statistical parametric speech synthesizer from these derived parameters. Altho...

What migration sets-out to do Deriving parameters for migration: 1D versus 3D assumptions Inversion Estimating image uncertainty Resolution scale length Generic model building loop for ray-based tomography Parametric versus non-parametric autopicking Wide azimuth and multi-azimuth data Anisotropic Model Building Anisotropic pre-stack depth migration in the absence of well control Resolving near...

Identification of protein complexes from proteomic experiments is crucial to understand not only their function but also the principles of cellular organization. Advances in experimental techniques have enabled the construction of large–scale protein–protein interaction networks, and computational methods have been developed to analyze high–throughput data. In most cases several parameters are ...