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Likelihood-Free Frequentist Inference: Confidence Sets with Correct Conditional Coverage

, , , , and . (2021)cite arxiv:2107.03920Comment: 59 pages, 14 figures, code available at https://github.com/Mr8ND/ACORE-LFI.

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Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameterization., and . IEEE Trans. Pattern Anal. Mach. Intell., 28 (9): 1393-1403 (2006)Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model., , and . Int. J. Comput. Vis., 41 (1/2): 35-59 (2001)The Nonlinear Statistics of High-Contrast Patches in Natural Images., , and . Int. J. Comput. Vis., 54 (1-3): 83-103 (2003)Likelihood-Free Frequentist Inference: Confidence Sets with Correct Conditional Coverage, , , , and . (2021)cite arxiv:2107.03920Comment: 59 pages, 14 figures, code available at https://github.com/Mr8ND/ACORE-LFI.Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference., , , , , and . CoRR, (2024)RFCDE: Random Forests for Conditional Density Estimation., and . CoRR, (2018)Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm., , , , and . CoRR, (2022)Toward a Full Probability Model of Edges in Natural Images., and . ECCV (1), volume 2350 of Lecture Notes in Computer Science, page 328-342. Springer, (2002)Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference., , , , , and . Astron. Comput., (2020)Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 2323-2334. PMLR, (2020)