203 citations · 2.2k across the 52 of their papers we have counts for
32 papers · 1 filter
Target-aware Bayesian inference via generalized thermodynamic integration
F. Llorente, L. Martino, D. Delgado
In Bayesian inference, we are usually interested in the numerical approximation of integrals that are posterior expectations or marginal likelihoods (a.k.a., Bayesian evidence). In…
Gradient-based Adaptive Importance Samplers
Víctor Elvira, Emilie Chouzenoux, Ömer Deniz Akyildiz +1
Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of intractable integrals, very often involving a target probability density function. The perfo…
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 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…
Deep Importance Sampling based on Regression for Model Inversion and Emulation
F. Llorente, L. Martino, D. Delgado +1
Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been wi…
Adaptive quadrature schemes for Bayesian inference via active learning
F. Llorente, L. Martino, V. Elvira +2
Numerical integration and emulation are fundamental topics across scientific fields. We propose novel adaptive quadrature schemes based on an active learning procedure. We consider…
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
Fernando Llorente, Luca Martino, David Delgado +1
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability m…
Importance Gaussian Quadrature
Víctor Elvira, Luca Martino, Pau Closas
Importance sampling (IS) and numerical integration methods are usually employed for approximating moments of complicated target distributions. In its basic procedure, the IS method…
A Review of Multiple Try MCMC algorithms for Signal Processing
Luca Martino
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computati…
Parsimonious Adaptive Rejection Sampling
Luca Martino
Monte Carlo (MC) methods have become very popular in signal processing during the past decades. The adaptive rejection sampling (ARS) algorithms are well-known MC technique which d…
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…
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…
Parallel Metropolis chains with cooperative adaptation
L. Martino, V. Elvira, D. Luengo +1
Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have become very popular in signal processing over the last years. In this work, we introduce a novel MCMC…
Adaptive Rejection Sampling with fixed number of nodes
L. Martino, F. Louzada
The adaptive rejection sampling (ARS) algorithm is a universal random generator for drawing samples efficiently from a univariate log-concave target probability density function (p…
Issues in the Multiple Try Metropolis mixing
L. Martino, F. Louzada
The multiple Try Metropolis (MTM) algorithm is an advanced MCMC technique based on drawing and testing several candidates at each iteration of the algorithm. One of them is selecte…
Orthogonal parallel MCMC methods for sampling and optimization
L. Martino, V. Elvira, D. Luengo +2
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov…
Efficient Multiple Importance Sampling Estimators
Víctor Elvira, Luca Martino, David Luengo +1
Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained…
Layered Adaptive Importance Sampling
L. Martino, V. Elvira, D. Luengo +1
Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizati…