Publications related to Maximum-Likelihood Identification of Sampled Gaussian Processes

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Michaël Unser, Julien René Pierre Fageot, Anaïs Laure Marie-Thérèse Badoual

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical ...

2018

Approximate maximum likelihood estimation for population genetic inference

Gregory Bruce Ewing

In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. Howev ...

De Gruyter2017

Likelihood Estimation for the INAR(p) Model by Saddlepoint Approximation

Anthony Christopher Davison

Saddlepoint techniques have been used successfully in many applications, owing to the high accuracy with which they can approximate intractable densities and tail probabilities. This article concerns their use for the estimation of high-order integer-value ...

American Statistical Association2015

Modeling the Importance of Faces in Natural Images

Sabine Süsstrunk, Cheryl Shaulya Lau, Appu Shaji, Gökhan Yildirim, Bin Jin

In this work we study the varying importance of faces in images. Face importance is found to be affected by the size and number of faces present. We collected a dataset of 152 face images with faces in different size and number of faces. We conducted a cro ...

Spie-Int Soc Optical Engineering2015

Extremal behavior of squared Bessel processes attracted by the Brown-Resnick process

Sebastian Engelke

The convergence of properly time-scaled and normalized maxima of independent standard Brownian motions to the Brown-Resnick process is well-known in the literature. In this paper, we study the extremal functional behavior of non-Gaussian processes, namely ...

Elsevier Science Bv2015

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Michaël Unser, John Paul Ward, Aurélien Bourquard, Hagay Kirshner

We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and sto ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Michaël Unser, Ulugbek Kamilov

We consider the estimation of an independent and identically distributed (i.i.d.) (possibly non-Gaussian) vector x is an element of R-n from measurements y is an element of R-m obtained by a general cascade model consisting of a known linear transform foll ...

Ieee-Inst Electrical Electronics Engineers Inc2014

On the Unique Identification of Continuous-Time Autoregressive Models From Sampled Data

Michaël Unser, John Paul Ward, Hagay Kirshner

In this work, we investigate the relationship between continuous-time autoregressive (AR) models and their sampled version. We consider uniform sampling and derive criteria for uniquely determining the continuous-time parameters from sampled data; the mode ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Michaël Unser, Julien René Pierre Fageot, Emrah Bostan

We study the statistics of wavelet coefficients of non-Gaussian images, focusing mainly on the behaviour at coarse scales. We assume that an image can be whitened by a fractional Laplacian operator, which is consistent with an

∥ω∥ ^{ -\gamma }

spectral ...

IEEE2014

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

Maximum-Likelihood Identification of Sampled Gaussian Processes

Graph Chatbot

Chat with Graph Search

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Approximate maximum likelihood estimation for population genetic inference

Likelihood Estimation for the INAR(p) Model by Saddlepoint Approximation

Modeling the Importance of Faces in Natural Images

Extremal behavior of squared Bessel processes attracted by the Brown-Resnick process

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

On the Unique Identification of Continuous-Time Autoregressive Models From Sampled Data

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Wavelet-variance-based estimation for composite stochastic processes

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

On the Unique Identification of Continuous-Time Autoregressive Models From Sampled Data

Approximate maximum likelihood estimation for population genetic inference

Modeling the Importance of Faces in Natural Images

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Extremal behavior of squared Bessel processes attracted by the Brown-Resnick process

Wavelet-variance-based estimation for composite stochastic processes

Likelihood Estimation for the INAR(p) Model by Saddlepoint Approximation

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling