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.
Journal of Machine Learning Research , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
The authors introduce an RJMCMC framework with prior, independence, and moment-matching proposals for non-conjugate ddCRP models, validated via simulation and Old Faithful eruption data.
citing papers explorer
-
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.
-
Bayesian Inference for Non-Conjugate Distance Dependent Chinese Restaurant Process Models
The authors introduce an RJMCMC framework with prior, independence, and moment-matching proposals for non-conjugate ddCRP models, validated via simulation and Old Faithful eruption data.