203 citations · 2.2k across the 52 of their papers we have counts for
6 papers · 1 filter
A Bayesian inference and model selection algorithm with an optimisation scheme to infer the model noise power
J. Lopez-Santiago, L. Martino, J. Miguez +1
Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate…
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…
Automatic tempered posterior distributions for Bayesian inversion problems
L. Martino, F. Llorente, E. Curbelo +2
We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. Mo…
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…
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…