226 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre de Recherche en Informatique, Signal et Automatique de LilleFR37 papers
- Université de LilleFR33 papers
- Centre National de la Recherche ScientifiqueFR26 papers
- Centre de Recherche en InformatiqueFR16 papers
- École Centrale de LilleFR13 papers
- Télécom LilleFR10 papers
- Institut Mines-TélécomFR8 papers
- Universidad Carlos III de MadridES5 papers
- University of FlorenceIT5 papers
- Stony Brook UniversityUS4 papers
- Universidad Rey Juan CarlosES4 papers
- Universitat de ValènciaES4 papers
10 papers · 1 filter
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models
Benjamin Cox, Emilie Chouzenoux, Victor Elvira
State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Inste…
A Survey of Monte Carlo Methods for Parameter Estimation
D. Luengo, L. Martino, M. Bugallo +2
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either…
Robust Covariance Adaptation in Adaptive Importance Sampling
Yousef El-Laham, Victor Elvira, Monica F. Bugallo
Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution. Ad…
Accelerating MCMC Algorithms
Christian P. Robert, Victor Elvira, Nick Tawn +1
Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heav…
Semi-independent resampling for particle filtering
Roland Lamberti, Yohan Petetin, François Desbouvries +1
Among Sequential Monte Carlo (SMC) methods,Sampling Importance Resampling (SIR) algorithms are based on Importance Sampling (IS) and on some resampling-based)rejuvenation algorithm…
Group Importance Sampling for Particle Filtering and MCMC
L. Martino, V. Elvira, G. Camps-Valls
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (…
The Recycling Gibbs Sampler for Efficient Learning
Luca Martino, Victor Elvira, Gustau Camps-Valls
Monte Carlo methods are essential tools for Bayesian inference. Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, mac…
Independent Resampling Sequential Monte Carlo Algorithms
Roland Lamberti, Yohan Petetin, François Desbouvries +1
Sequential Monte Carlo algorithms, or Particle Filters, are Bayesian filtering algorithms which propagate in time a discrete and random approximation of the a posteriori distributi…
Generalized Multiple Importance Sampling
Víctor Elvira, Luca Martino, David Luengo +1
Importance Sampling methods are broadly used to approximate posterior distributions or some of their moments. In its standard approach, samples are drawn from a single proposal dis…
Adapting the Number of Particles in Sequential Monte Carlo Methods through an Online Scheme for Convergence Assessment
Víctor Elvira, Joaquín Míguez, Petar M. Djurić
Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the…