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We propose using model reparametrization to improve variational Bayes inference for hierarchical models whose variables can be classified as global (shared across observations) or local (observation specific).
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Matrix differential calculus with applications in statistics and econometrics
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Non-centered parameterisations for hierarchical models and data augmentation
Papaspiliopoulos, O., G. O. Roberts, and M. Sköld (2003) · 2003
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A general framework for the parametrization of hierarchical models
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Variational inference for generalized linear mixed models using partially non-centered parametrizations
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Auto-encoding variational Bayes
Kingma, D. P. and M. Welling (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
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Stochastic structured variational inference
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Variational inference with normalizing flows
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Learning model reparametrizations: Implicit variational inference by fitting mcmc distributions
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Gaussian variational approximation with sparse precision matrices
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