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Normalizing-flow variational inference enriches the variational family by transforming a simple base distribution through a sequence of invertible, differentiable mappings.
MCMC simulates a Markov chain whose long-run behavior matches a target distribution, letting us sample from complex posteriors without knowing the normalization constant.