203 citations · 2.2k across the 52 of their papers we have counts for
6 papers · 1 filter
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…
Multi-label Methods for Prediction with Sequential Data
Jesse Read, Luca Martino, Jaakko Hollmén
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of cla…
Cooperative Parallel Particle Filters for online model selection and applications to Urban Mobility
Luca Martino, Jesse Read, Victor Elvira +1
We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modal…
Heretical Multiple Importance Sampling
Víctor Elvira, Luca Martino, David Luengo +1
Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the imp…
Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes
Víctor Elvira, Luca Martino, David Luengo +1
Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in natu…
Effective Sample Size for Importance Sampling based on discrepancy measures
L. Martino, V. Elvira, F. Louzada
The Effective Sample Size (ESS) is an important measure of efficiency of Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. In the…