Publication

Bayesian Inference For The Brown-Resnick Process, With An Application To Extreme Low Temperatures

Publications associées (59)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

Variational Gaussian Inference for Bilinear Models of Count Data

Mohammad Emtiyaz Khan, Young Jun Ko

Bilinear models of count data with Poisson distribution are popular in applications such as matrix factorization for recommendation systems, modeling of receptive fields of sensory neurons, and modeling of neural-spike trains. Bayesian inference in such mo ...

2014

Efficient inference for spatial extreme value processes associated to log-Gaussian random functions

Jonathan A. Tawn, Jennifer Lynne Wadsworth

Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, infere ...

Oxford University Press2014

Distributed inference over regression and classification models

Ali H. Sayed

We study the distributed inference task over regression and classification models where the likelihood function is strongly log-concave. We show that diffusion strategies allow the KL divergence between two likelihood functions to converge to zero at the r ...

IEEE2013

To improve visual tracking, a large number of papers study more powerful features, or better cue fusion mechanisms, such as adaptation or contextual models. A complementary approach consists of improving the track management, that is, deciding when to add ...

2013

A fast mean field variational Bayes (MFVB) approach to nonparametric regression when the predictors are subject to classical measurement error is investigated. It is shown that the use of such technology to the measurement error setting achieves reasonable ...

Elsevier2013

Robust and Private Bayesian Inference

Christos Dimitrakakis, Aikaterini Mitrokotsa

We examine the robustness and privacy properties of Bayesian inference under assumptions on the prior, but without any modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome ...

2013

Bayesian inference from composite likelihoods, with an application to spatial extremes

Anthony Christopher Davison

Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although some frequentist properties of the maximum composite likelihood estimator are akin to those of the maximu ...

Academia Sinica, Institute of Statistical Science2012

A Comparative Study of Time Delay Estimation Techniques for Road Vehicle Tracking

Hervé Lissek, Xavier Falourd, Patrick Marmaroli

This paper addresses road traffic monitoring using passive acoustic sensors. Recently, the feasibility of the joint speed and wheelbase length estimation of a road vehicle using particle filtering has been demonstrated. In essence, the direction of arrival ...

2012

Bayesian detection of asynchronous EEG patterns

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Nicolas Bourdaud

We proposed a Bayesian model for the detection of asynchronous EEG patterns. Based on a skew normal model of the pattern of interest in the time-domain and on the assumption that background activity can be modeled as colored noise, we estimate both the pat ...

2011

Context models on sequences of covers

Christos Dimitrakakis

We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and m ...

2011