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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A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.
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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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A multi-eigenbasis approach to covariance matrix denoising for cosmological inference
A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.