80 citations · 164 across the 13 of their papers we have counts for
13 papers
A note on the area under the likelihood and the fake evidence for model selection
L. Martino, F. Llorente
Improper priors are not allowed for the computation of the Bayesian evidence (a.k.a., marginal likelihood), since in this case is not completely specified due to…
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data
Samuel Rey, Ernesto Curbelo, Luca Martino +2
This work addresses the problem of graph learning from data following a Gaussian Graphical Model (GGM) with a time-varying mean. Graphical Lasso (GL), the standard method for estim…
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…
Optimality in importance sampling: a gentle survey
Fernando Llorente, Luca Martino
The performance of the Monte Carlo sampling methods relies on the crucial choice of a proposal density. The notion of optimality is fundamental to design suitable adaptive procedur…
Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals
E. Curbelo, L. Martino, F. Llorente +1
In this paper we address the problem of performing Bayesian inference for the parameters of a nonlinear multi-output model and the covariance matrix of the different output signals…
An exhaustive variable selection study for linear models of soundscape emotions: rankings and Gibbs analysis
R. San Millán-Castillo, L. Martino, E. Morgado +1
In the last decade, soundscapes have become one of the most active topics in Acoustics, providing a holistic approach to the acoustic environment, which involves human perception a…
On the safe use of prior densities for Bayesian model selection
F. Llorente, L. Martino, E. Curbelo +2
The application of Bayesian inference for the purpose of model selection is very popular nowadays. In this framework, models are compared through their marginal likelihoods, or the…
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
Simo Alami. C, Fernando Llorente, Rim Kaddah +2
Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal…
Optimality in Noisy Importance Sampling
Fernando Llorente, Luca Martino, Jesse Read +1
In this work, we analyze the noisy importance sampling (IS), i.e., IS working with noisy evaluations of the target density. We present the general framework and derive optimal prop…
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…
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…