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Approximate Bayesian Inference
Article

Variationally Inferred Sampling through a Refined Bound

1
Institute of Mathematical Sciences (ICMAT), 28049 Madrid, Spain
2
Statistical and Applied Mathematical Sciences Institute, Durham, NC 7333, USA
3
School of Management, University of Shanghai for Science and Technology, Shanghai 201206, China
*
Author to whom correspondence should be addressed.
Entropy 2021, 23(1), 123; https://doi.org/10.3390/e23010123
Received: 24 December 2020 / Revised: 9 January 2021 / Accepted: 13 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Approximate Bayesian Inference)
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 state-space models for time-series data, a variational encoder for density estimation and a conditional variational autoencoder as a deep Bayes classifier. View Full-Text
Keywords: variational inference; MCMC; stochastic gradients; neural networks variational inference; MCMC; stochastic gradients; neural networks
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MDPI and ACS Style

Gallego, V.; Ríos Insua, D. Variationally Inferred Sampling through a Refined Bound. Entropy 2021, 23, 123. https://doi.org/10.3390/e23010123

AMA Style

Gallego V, Ríos Insua D. Variationally Inferred Sampling through a Refined Bound. Entropy. 2021; 23(1):123. https://doi.org/10.3390/e23010123

Chicago/Turabian Style

Gallego, Víctor, and David Ríos Insua. 2021. "Variationally Inferred Sampling through a Refined Bound" Entropy 23, no. 1: 123. https://doi.org/10.3390/e23010123

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