RT Journal Article
T1 Variationally inferred sampling through a refined bound
A1 Gallego, Víctor
A1 Rios Insua, David
AB In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation. We call this framework “refined variational approximation”. Its strengths are its ease of implementation and the automatic tuning of sampler parameters, leading to a faster mixing time through automatic differentiation. Several strategies to approximate evidence lower bound (ELBO) computation are also introduced. Its efficient performance is showcased experimentally using statespace models for time-series data, a variational encoder for density estimation and a conditional variational autoencoder as a deep Bayes classifier.
PB MDPI
SN 1099-4300
YR 2021
FD 2021-01-19
LK https://hdl.handle.net/20.500.14352/7298
UL https://hdl.handle.net/20.500.14352/7298
LA eng
NO Ministerio de Economía y Competitividad (MINECO)
NO Centro de Excelencia Severo Ochoa
NO National Science Foundation
DS Docta Complutense
RD 21 abr 2025