80 citations · 164 across the 13 of their papers we have counts for
7 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…
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…
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…