203 citations · 2.2k across the 52 of their papers we have counts for
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Fully Adaptive Gaussian Mixture Metropolis-Hastings Algorithm
David Luengo, Luca Martino
Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an effici…
Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains
Jesse Read, Luca Martino, David Luengo
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional…
Improved Adaptive Rejection Metropolis Sampling Algorithms
Luca Martino, Jesse Read, David Luengo
Markov Chain Monte Carlo (MCMC) methods, such as the Metropolis-Hastings (MH) algorithm, are widely used for Bayesian inference. One of the most important issues for any MCMC metho…
On the Generalized Ratio of Uniforms as a Combination of Transformed Rejection and Extended Inverse of Density Sampling
Luca Martino, David Luengo, Joaquín Míguez
In this work we investigate the relationship among three classical sampling techniques: the inverse of density (Khintchine's theorem), the transformed rejection (TR) and the genera…
On the flexibility of the design of Multiple Try Metropolis schemes
Luca Martino, Jesse Read
The Multiple Try Metropolis (MTM) method is a generalization of the classical Metropolis-Hastings algorithm in which the next state of the chain is chosen among a set of samples, a…