mlsanityData to 14 Oct 2026

Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network

arXiv 1510.02175 ↗First posted 8 Oct 2015stat.ME stat.CO stat.MLcited by 39

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Abstract (arXiv)

Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy and computational efficiency of ABC depend on the choice of summary statistic, but outside of special cases where the optimal summary statistics are known, it is unclear which guiding principles can be used to construct effective summary statistics. In this paper we explore the possibility of automating the process of constructing summary statistics by training deep neural networks to predict the parameters from artificially generated data: the resulting summary statistics are approximately posterior means of the parameters. With minimal model-specific tuning, our method constructs summary statistics for the Ising model and the moving-average model, which match or exceed theoretically-motivated summary statistics in terms of the accuracies of the resulting posteriors.

Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Oct 2015.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 8; 2024: 6; 2025: 7; 2026: 2.

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