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This is a Quantitative Study study.

October 19, 2025Open Access

Variational Inference for Latent Variable Models in High Dimensions

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Framework quantifies statistical accuracy of variational inference in latent variable models, indicating improvements in posterior approximation.

Mean-field variational inference successfully approximates posteriors in specific latent variable contexts, revealing its effectiveness.
A proposed partially grouped variational inference algorithm outperforms the standard fully factorized approach for the stochastic blockmodel.
Exact finite-sample performance bounds confirm the efficiency of the new approach when applied to key models like the latent Dirichlet allocation.
Techniques developed extend beyond the current models, opening pathways for broader application of variational inference in Bayesian analysis.

Zhong et al. (2025) studied this question.

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Framework quantifies statistical accuracy of variational inference in latent variable models, indicating improvements in posterior approximation.