5 papers · 1 filter
Importance sampling for Bayesian inference: polynomial-dimension dependent error bounds
Fabián González, VÃctor Elvira, JoaquÃn MÃguez
Many Bayesian inference problems involve high-dimensional models where the performance of standard importance sampling (IS) methods often degrades rapidly as the dimensionality inc…
A novel sequential method for building upper and lower bounds of moments of distributions
Solal Martin, Emilie Chouzenoux, Victor Elvira
Approximating integrals is a fundamental task in probability theory and statistical inference, and their applied fields of signal processing, and Bayesian learning, as soon as expe…
Effective sample size approximations as entropy measures
L. Martino, V. Elvira
In this work, we analyze alternative effective sample size (ESS) metrics for importance sampling algorithms, and discuss a possible extended range of applications. We show the rela…
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms
Thomas Guilmeau, Emilie Chouzenoux, VÃctor Elvira
We study the variational inference problem of minimizing a regularized Rényi divergence over an exponential family. We propose to solve this problem with a Bregman proximal gradie…
On variational inference and maximum likelihood estimation with the λ-exponential family
Thomas Guilmeau, Emilie Chouzenoux, VÃctor Elvira
The λ-exponential family has recently been proposed to generalize the exponential family. While the exponential family is well-understood and widely used, this it not the case of…