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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

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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BibTeX

@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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