Probabilistic Models with Deep Neural Networks

Probabilistic Models with Deep Neural Networks

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational...

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Bibliographic Details
Main Authors: Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Entropy
Subjects:
deep probabilistic modeling
variational inference
neural networks
latent variable models
Bayesian learning
Online Access:https://www.mdpi.com/1099-4300/23/1/117

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