6 papers
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…
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…
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…
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
F. Llorente, L. Martino, J. Read +1
This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities which are intractable, costly, and/or noisy. This type of problem can…