V́ıctor Elvira

A new strategy for effective learning in population Monte Carlo sampling

2016

In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new …

Anti-tempered layered adaptive importance sampling

2017

Monte Carlo (MC) methods are widely used for Bayesian inference in signal processing, machine learning and statistics. In this work, we introduce an adaptive importance sampler which mixes together the benefits of the Importance Sampling …

Optimized Auxiliary Particle Filters

2020 · arXiv (Cornell University)

Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for Bayesian inference in state-space models. In their original derivation, APFs operate in an extended space using an auxiliary variable to improve …

Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations

2022 · arXiv (Cornell University)

In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO). We propose the multiple importance sampling ELBO (MISELBO), a \textit{versatile} …