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  • 标题:Mining gold from implicit models to improve likelihood-free inference
  • 本地全文:下载
  • 作者:Johann Brehmer ; Gilles Louppe ; Juan Pavez
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2020
  • 卷号:117
  • 期号:10
  • 页码:5242-5249
  • DOI:10.1073/pnas.1915980117
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Simulators often provide the best description of real-world phenomena. However, the probability density that they implicitly define is often intractable, leading to challenging inverse problems for inference. Recently, a number of techniques have been introduced in which a surrogate for the intractable density is learned, including normalizing flows and density ratio estimators. We show that additional information that characterizes the latent process can often be extracted from simulators and used to augment the training data for these surrogate models. We introduce several loss functions that leverage these augmented data and demonstrate that these techniques can improve sample efficiency and quality of inference.
  • 关键词:simulation-based inference ; implicit models ; neural density estimation
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