Variationally Inferred Sampling through a Refined Bound

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 implemen...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2021-01, Vol.23 (1), p.123
Main Authors: Gallego, Víctor, Ríos Insua, David
Format: Article
Language:English
Subjects:
Approximation
Bayesian analysis
Coders
Flexibility
Lower bounds
Markov chains
MCMC
Neural networks
Optimization
Probabilistic inference
Probabilistic models
State space models
Statistical inference
stochastic gradients
Stochastic models
variational inference
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e23010123