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.
A new uncertainty importance measure.Reliability Engineering & System Safety, 92(6):771–784, 2007
2 Pith papers cite this work. Polarity classification is still indexing.
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A multi-layer uncertainty model finds that both transmission-layer and over-the-top exceptional-access architectures carry strictly higher modeled compromise risk than no-EA baselines, with risk distributions differing by architecture class under sparse evidence.
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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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Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence
A multi-layer uncertainty model finds that both transmission-layer and over-the-top exceptional-access architectures carry strictly higher modeled compromise risk than no-EA baselines, with risk distributions differing by architecture class under sparse evidence.