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For questions related to variational inference (VI), an optimization-based approach to the inference problem (i.e. the computation of the posterior given the prior, likelihood, and marginal). VI is used, for example, in the context of auto-encoders (VAEs) and Bayesian neural networks (BNNs).

For more info, you could read the paper Variational Inference: A Review for Statisticians (2018) by David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe.