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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4 Pith papers cite this work. Polarity classification is still indexing.
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PoE-Bridge uses a product-of-experts bridge between diffusion and autoregressive distributions, with DLM drafting plus rejection and importance sampling, to deliver 5x speedup over standard DLM decoding while recovering at least 95% of AR performance on math and coding tasks.
Mass ratio reversals produce qualitatively different contributions to BBH merger rates and masses in COMPAS versus SEVN simulations, with core-growth dominating and most systems arising from massive low-metallicity progenitors.
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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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.