High-Dimensional Copula Variational Approximation Through Transformation

DOI10.1080/10618600.2020.1740097OpenAlexW3011674709MaRDI QIDQ5066743

Michael Stanley Smith, David J. Nott, Rubén Loaiza-Maya

Publication date: 30 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1904.07495

zbMATH Keywords

Yeo-Johnson transformationfactor variational approximationimplicit copulainverse G\&H transformationskew-normal copula

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (6)

Fast and accurate variational inference for models with many latent variables ⋮ Marginally calibrated response distributions for end-to-end learning in autonomous driving ⋮ Implicit Copula Variational Inference ⋮ An Approximated Collapsed Variational Bayes Approach to Variable Selection in Linear Regression ⋮ Variational inference for cutting feedback in misspecified models ⋮ Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration

Uses Software

• bfa
• Stan
• aplore3
• QRM
• ADVI
• ADADELTA

Cites Work

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• A Primer on Copulas for Count Data
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