Variationally Inferred Sampling through a Refined Bound
MDPI – Entropy, 2022
Abstract
This study introduces a «refined variational approximation» framework, which enhances the efficiency of Bayesian inference in probabilistic models by embedding a Markov chain sampler within a variational posterior approximation.
Notably easy to implement, it elevates the mixing speed by tuning sampler parameters using automatic differentiation.
A new framework that enhances Bayesian inference in probabilistic models enhancing the efficiency of decision-making processes in various fields including financial analysis, risk management, machine learning, and logistics.
Where does it apply?
It can be applied in financial and risk analysis, where state-space time series models are particularly useful, data science for machine learning applications like variational autoencoders, and various other sectors that rely on data-informed decision making, such as logistics, energy, healthcare, and technology sectors among others.
Why does it matters?
It allows for efficient large-scale Bayesian inference in probabilistic models, providing a general approach to tuning Markov chain Monte Carlo sampler parameters.
This innovation improves the applicability of models, such as state-space time series and variational autoencoders. Furthermore, low-cost, convenient evidence lower bound (ELBO) approximations are an integral part of this achievement.
These advancements could potentially optimize Bayesian modeling, thus significantly impacting inference, prediction, and decision-making processes in various fields.
Variationally Inferred Sampling through a Refined Bound
MDPI – Entropy, 2022
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