203 citations · 2.2k across the 52 of their papers we have counts for
7 papers · 1 filter
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…
Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises
J. Read, L. Martino, P. Olmos +1
Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier…