Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
and Casella, George , year =
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Symmetries between target and variational family guarantee recovery of identifiable statistics in variational inference, unifying prior results and extending to von Mises-Fisher families on the sphere.
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Error Bounds for Importance Sampling with Estimated Proposal Distributions
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
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Symmetry Guarantees Statistic Recovery in Variational Inference
Symmetries between target and variational family guarantee recovery of identifiable statistics in variational inference, unifying prior results and extending to von Mises-Fisher families on the sphere.