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Parameter Determination of Sensor Stochastic Models under Covariate Dependency

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Data-driven tuning of linear parameter varying precompensators

Alireza Karimi, Mark Edward John Butcher

Methods for direct data-driven tuning of the parameters of precompensators for LPV systems are developed. Since the commutativity property is not always satisfied for LPV systems, previously proposed methods for LTI systems that use this property cannot be ...

Wiley-Blackwell2010

On ANOVA expansions and strategies for choosing the anchor point

Jan Sickmann Hesthaven, Zhen Gao

The classic Lebesgue ANOVA expansion offers an elegant way to represent functions that depend on a high-dimensional set of parameters and it often enables a substantial reduction in the evaluation cost of such functions once the ANOVA representation is con ...

Elsevier2010

Robust inference for generalized linear models

Sahar Hosseinian Ehrensberger

Generalized Linear Models have become a commonly used tool of data analysis. Such models are used to fit regressions for univariate responses with normal, gamma, binomial or Poisson distribution. Maximum likelihood is generally applied as fitting method. I ...

EPFL2009

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Martin Vetterli, Amin Karbasi, Ali Hormati

Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several i ...

2009

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Martin Vetterli, Amin Karbasi, Ali Hormati

2009

A MAP Approach to Noise Compensation of Speech

Philip Neil Garner

We show that estimation of parameters for the popular Gaussian model of speech in noise can be regularised in a Bayesian sense by use of simple prior distributions. For two example prior distributions, we show that the marginal distribution of the uncorrup ...

Idiap2009

Scalable and convergent multi-robot passive and active sensing

A major barrier preventing the wide employment of mobile networks of robots in tasks such as exploration, mapping, surveillance, and environmental monitoring is the lack of efficient and scalable multi-robot passive and active sensing (estimation) methodol ...

IEEE2009

Identiﬁcation and the information matrix: how to get just sufﬁciently rich?

Ljubisa Miskovic

In prediction error identiﬁcation, the information matrix plays a central role. Speciﬁcally, when the system is in the model set, the covariance matrix of the parameter estimates converges asymptotically, up to a scaling factor, to the inverse of the infor ...

2009

Dynamic modeling of the TOR signaling pathway in yeast

Irina Krier

This Master thesis presents a complete cycle in the use of models for the study of a complex pathway like the TOR pathway. Previous models were reproduced for the phosphatase part of the pathway, and the assumptions used and the results they provided were ...

2009

Global Continuation for Quasilinear Elliptic Systems on R-N and the Equations of Elastostatics

Charles Stuart, Hicham Georges Gebran

We consider quasilinear systems of second order elliptic equations on R-N. Using a continuation theorem based on the topological degree for C-1-Fredholm maps, we derive global properties of a maximal connected set of solutions which decay exponentially to ...

2009