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Maximum-Likelihood Identification of Sampled Gaussian Processes

Publications associées (55)

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Likelihood estimation of the extremal index

Mária Süveges

The article develops the approach of Ferro and Segers (2003) to the estimation of the extremal index, and proposes the use of a new variable decreasing the bias of the likelihood based on the point process character of the exceedances. Two estimators are d ...

2007

Likelihood estimation of the extremal index

Mária Süveges

Springer, Springer US; http://www.springer-ny.com2007

Likelihood estimation of the extremal index

Mária Süveges

A new likelihood method to estimate the extremal index, together with an application to temperature data, is presented. Conditions for the validity of the model are discussed and diagnostics are proposed. ...

2007

Robust estimation, interpretation and assessment of likelihood ratios in forensic speaker recognition

Andrzej Drygajlo

In this contribution, the Bayesian framework for interpretation of evidence when applied to forensic speaker recognition is introduced. Different aspects of the use of voice as evidence in the court are addressed, as well as the use by the forensic expert ...

2006

Non-Ideal Sampling and Adapted Reconstruction Using the Stochastic Matérn Model

Michaël Unser, Dimitri Nestor Alice Van De Ville, Sathish Ramani

The Matérn class is a parametric family of autocorrelation functions that is commonly used in geostatistics. We argue that a generalized, anisotropic version of this model is suitable for capturing the correlation structure of a variety of natural images. ...

IEEE2006

Sampling and Reconstruction of Signals with Finite Rate of Innovation in the Presence of Noise

Martin Vetterli, Irena Maravic

Recently, it was shown that it is possible to develop exact sampling schemes for a large class of parametric nonban- dlimited signals, namely certain signals of finite rate of innovation. A common feature of such signals is that they have a finite number o ...

2005

Embedding Motion in Model-Based Stochastic Tracking

Daniel Gatica-Perez, Jean-Marc Odobez, Silèye Oumar Ba

Particle filtering is now established as one of the most popular method for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states. In this paper ...

IDIAP2003

Conditional Gaussian Mixtures

I show how conditional Gaussians, whose means are conditioned by a random variable, can be estimated and their likelihoods computed. This is based upon how regular Gaussians have their own parameters and likelihood computed. After explaining how to estimat ...

IDIAP2003

Transient behavior of affine projection algorithms

Ali H. Sayed

Most analytical results on affine projection algorithms assume special regression models or Gaussian regression data. The available analyses also treat different affine projection filters separately. This paper provides a unified treatment of the transient ...

2003

Sampling innovations

Pina Marziliano

Sampling theory has prospered extensively in the last century. The elegant mathematics and the vast number of applications are the reasons for its popularity. The applications involved in this thesis are in signal processing and communications and call out ...

EPFL2002