Gallego, VíctorRios Insua, David2023-06-172023-06-172021-01-191099-430010.3390/e23010123https://hdl.handle.net/20.500.14352/7298In 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.engjournal articlehttps://doi.org/10.3390/e23010123open access519.21Variational inferenceMCMCStochastic gradientsNeural networksProbabilidades (Matemáticas)