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A variational neural Bayes framework for inference on intractable posterior distributions

E Maceda, EC Hector, A Lenzi, BJ Reich - arXiv preprint arXiv:2404.10899, 2024 - arxiv.org
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Classic Bayesian methods with complex models are frequently infeasible due to an intractable likelihood. Simulation-based inference methods, such as Approximate …

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@misc{maceda2024variationalneuralbayesframework,
title={A variational neural Bayes framework for inference on intractable posterior distributions},
author={Elliot Maceda and Emily C. Hector and Amanda Lenzi and Brian J. Reich},
year={2024},
eprint={2404.10899},
archivePrefix={arXiv},
primaryClass={stat.CO},
url={https//arxiv.org/abs/2404.10899},
}

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