Optimality in Noisy Importance Sampling
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Abstract (arXiv)
In this work, we analyze the noisy importance sampling (IS), i.e., IS working with noisy evaluations of the target density. We present the general framework and derive optimal proposal densities for noisy IS estimators. The optimal proposals incorporate the information of the variance of the noisy realizations, proposing points in regions where the noise power is higher. We also compare the use of the optimal proposals with previous optimality approaches considered in a noisy IS framework.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 34 references that are in the map. First posted on arXiv 7 Jan 2022.
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13 of 34 references.
References · 13
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- 2015On Markov chain Monte Carlo methods for tall datacited by 50
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- 2021A Survey of Monte Carlo Methods for Parameter Estimationcited by 23
- 2014Speeding Up MCMC by Efficient Data Subsamplingcited by 18
- 2019Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluationscited by 18
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- 2021Ensemble Inference Methods for Models With Noisy and Expensive Likelihoodscited by 8
- 2021A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABCcited by 8
- 2020Adaptive quadrature schemes for Bayesian inference via active learningcited by 6
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.
2 papers.