203 citations · 1.6k across the 78 of their papers we have counts for
6 papers · 1 filter
A principled stopping rule for importance sampling
Medha Agarwal, Dootika Vats, Víctor Elvira
Importance sampling (IS) is a Monte Carlo technique that relies on weighted samples, simulated from a proposal distribution, to estimate intractable integrals. The quality of the e…
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…
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
Luca Martino, Víctor Elvira, Javier López-Santiago +1
In many inference problems, the evaluation of complex and costly models is often required. In this context, Bayesian methods have become very popular in several fields over the las…
Compressed Monte Carlo with application in particle filtering
Luca Martino, Víctor Elvira
Bayesian models have become very popular over the last years in several fields such as signal processing, statistics, and machine learning. Bayesian inference requires the approxim…
MCMC-driven importance samplers
F. Llorente, E. Curbelo, L. Martino +2
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we…
Advances in Importance Sampling
Víctor Elvira, Luca Martino
Importance sampling (IS) is a Monte Carlo technique for the approximation of intractable distributions and integrals with respect to them. The origin of IS dates from the early 195…