{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/minimizing-the-expected-posterior-entropy","title":"Unifying Summary Statistic Selection for Approximate Bayesian Computation","arxiv_id":"2206.02340","date":"2022-06-06","proceeding":null,"authors":["Till Hoffmann","Jukka-Pekka Onnela"],"abstract":"Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize different classes of summaries and demonstrate their importance for correctly analysing dimensionality reduction algorithms. 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