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A comparison of mixture models for density estimation

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Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures

Sylvain Calinon

The chapter presents an approach for the interactive definition of curves and motion paths based on Gaussian mixture model (GMM) and optimal control. The input of our method is a mixture of multivariate Gaussians defined by the user, whose centers define a ...

Springer2019

Recovering covariance from functional fragments

Victor Panaretos, Marie-Hélène Descary

We consider nonparametric estimation of a covariance function on the unit square, given a sample of discretely observed fragments of functional data. When each sample path is observed only on a subinterval of length , one has no statistical information on ...

2019

A design method for distributed luenberger observers

Colin Neil Jones, Francisco Fernandes Castro Rego

The paper addresses the problem of designing distributed observers for discrete linear time-invariant (LTI) systems with distributed sensor nodes subjected to bounded measurement noise. A solution is proposed in terms of a distributed LTI Luenberger observ ...

IEEE2018

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Rudolf Haraksim

This study presents a method for computing likelihood ratios (LRs) from multimodal score distributions, as the ones produced by some commercial off-the-shelf automated fingerprint identification systems (AFISs). The AFIS algorithms used to compare fingerma ...

Inst Engineering Technology-IET2017

Estimation of Space-Time Varying Parameters Using a Diffusion LMS Algorithm

Ali H. Sayed

We study the problem of distributed adaptive estimation over networks where nodes cooperate to estimate physical parameters that can vary over both space and time domains. We use a set of basis functions to characterize the space-varying nature of the para ...

2014

Wavelet-variance-based estimation for composite stochastic processes

Jan Skaloud, Stéphane Guerrier, Yannick Stebler

This article presents a new estimation method for the parameters of a time series model. We consider here composite Gaussian processes that are the sum of independent Gaussian processes which, in turn, explain an important aspect of the time series, as is ...

Amer Statistical Assoc2013

On the probability distribution of distributed optimization strategies

Ali H. Sayed

We study the steady-state probability distribution of diffusion and consensus strategies that employ constant step-sizes to enable continuous adaptation and learning. We show that, in the small step-size regime, the estimation error at each agent approache ...

IEEE2013

Nonparametric Construction of Multivariate Kernels

Victor Panaretos, Kjell Konis

We propose a nonparametric method for constructing multivariate kernels tuned to the configuration of the sample, for density estimation in R-d, d moderate. The motivation behind the approach is to break down the construction of the kernel into two parts: ...

American Statistical Association2012

Mode choice with attitudinal latent class: a Swiss case-study

Michel Bierlaire, Aurélie Glerum, Bilge Atasoy

Attitudinal attributes play an important role in behavior of individuals in various contexts. In this study we focus on the travel behavior and our main objective is to come up with segments of individuals that have different mode choice preferences with t ...

2011

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