Sampling the Variational Posterior with Local Refinement

Variational inference is an optimization-based method for approximating the posterior distribution of the parameters in Bayesian probabilistic models. A key challenge of variational inference is to approximate the posterior with a distribution that is computationally tractable yet sufficiently expre...

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Bibliographic Details
Published in:Entropy
Main Authors: Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Subjects:
bayesian inference
variational inference
deep neural networks
contextual bandits
Online Access:https://www.mdpi.com/1099-4300/23/11/1475

Internet

https://www.mdpi.com/1099-4300/23/11/1475

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