NeurIPS 2020poster56 citations
Variational Bayesian Monte Carlo with Noisy Likelihoods
Abstract
Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods. In this work, we extend VBMC to deal with noisy log-likelihood evaluations, such as those arising from simulation-based models. We introduce new
BibTeX
@inproceedings{NEURIPS2020_5d409541,
author = {Acerbi, Luigi},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {8211--8222},
publisher = {Curran Associates, Inc.},
title = {Variational Bayesian Monte Carlo with Noisy Likelihoods},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/5d40954183d62a82257835477ccad3d2-Paper.pdf},
volume = {33},
year = {2020}
}