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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Stationary MMD points show super-convergence in integration error over MMD for RKHS integrands, and MMD gradient flows compute them with a new non-asymptotic finite-particle error bound.
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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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Stationary MMD Points
Stationary MMD points show super-convergence in integration error over MMD for RKHS integrands, and MMD gradient flows compute them with a new non-asymptotic finite-particle error bound.