Sampling the Variational Posterior with Local Refinement

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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Detalles Bibliográficos
Autores principales: Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
bayesian inference
variational inference
deep neural networks
contextual bandits
Science
Q
Astrophysics
QB460-466
Physics
QC1-999
Acceso en línea:https://doaj.org/article/cefaef5f24ee49009bbd835ef6ef3eca
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https://doaj.org/article/cefaef5f24ee49009bbd835ef6ef3eca

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