output
20152020
most citedGeneralized Multiple Importance Sampling

144 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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5 papers · 1 filter

stat.CO2016★ 34 cited

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…

stat.CO2016★ 23 cited

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…

stat.CO2015★ 144 cited

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…

stat.CO2015★ 3 cited

How Can Subsampling Reduce Complexity in Sequential MCMC Methods and Deal with Big Data in Target Tracking?

Allan De Freitas, François Septier, Lyudmila Mihaylova +1

Target tracking faces the challenge in coping with large volumes of data which requires efficient methods for real time applications. The complexity considered in this paper is whe…

stat.CO2015★ 66 cited

Langevin and Hamiltonian based Sequential MCMC for Efficient Bayesian Filtering in High-dimensional Spaces

Francois Septier, Gareth W. Peters

Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation…