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Improved Marginal Unbiased Score Expansion (MUSE) via Implicit Differentiation

M Millea - arXiv preprint arXiv:2209.10512, 2022 - arxiv.org
Statistics paper stat.ML Suggest

… Owing to its reliance on prior samples, MUSE can be considered a form of simulation-based inference, extended to use readily available joint posterior gradients, similar to the proposal …

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BibTeX

@misc{millea2022improved,
title={Improved Marginal Unbiased Score Expansion (MUSE) via Implicit Differentiation},
author={Marius Millea},
year={2022},
eprint={2209.10512},
archivePrefix={arXiv},
primaryClass={id='stat.ML' full_name='Machine Learning' is_active=True alt_name=None in_archive='stat' is_general=False description='Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding'}
}

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