Papers › Unifying Summary Statistic Selection for Approximate Bayesian Computation

Unifying Summary Statistic Selection for Approximate Bayesian Computation

6 Jun 2022arXiv:2206.02340archive 2025-07-28

Till Hoffmann, Jukka-Pekka Onnela

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. We demonstrate that minimizing the expected posterior entropy (EPE) under the prior predictive distribution of the model subsumes many existing methods. They are equivalent to or are special or limiting cases of minimizing the EPE. We offer a unifying framework for obtaining informative summaries, provide concrete recommendations for practitioners, and propose a practical method to obtain high-fidelity summaries whose utility we demonstrate for both benchmark and practical examples.

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