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Random Surface Covariance Estimation by Shifted Partial Tracing

Publications associées (37)

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Distributed bearing estimation via matrix completion

Volkan Cevher

We consider bearing estimation of multiple narrow-band plane waves impinging on an array of sensors. For this problem, bearing estimation algorithms such as minimum variance distortionless response (MVDR), multiple signal classification, and maximum likeli ...

2010

Exponential Natural Evolution Strategies

Daniël Pieter Wierstra, Tom Schaul

The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization by following the natural gradient of the expected fitness. Like the well-known CMA-ES, the most competitive algorithm in the field, NES c ...

2010

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

General state-space models

Thomas Gsponer

In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...

EPFL2004

The Odometry Error of a Mobile Robot with a Synchronous Drive System

This paper presents an error modeling of an odometry system for a synchronous-drive system and a possible strategy for evaluating this error. The odometry error is modeled by introducing four parameters characterizing its systematic and nonsystematic compo ...

2002

Maximum Likelihood Estimation of Hidden Markov Model Parameters, with Application to Medical Image Segmentation

Andrea Ridolfi

We address the problem of a non-supervised bidimensional segmentation method based upon a discrete-level unilateral Markov field model for the labels and conditionaly Gaussian densities for the observed voxels. Such models have been shown to yield numerica ...

1997

An approximately minimum variance jackknife

David Andrew Barry

Jackknifing is a nonparametric method of reducing bias in estimation procedures. The reduced-bias jackknife estimate is not, in general, a minimum variance (MV) estimate. The generalized jackknife is extended to allow the computation of jackknife estimates ...

Taylor & Francis1991