Abstrakti

Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summary statistics of observed and simulated data, the choice of the statistics is crucial. This choice involves a trade-off between loss of information and dimensionality reduction, and is often determined based on domain knowledge. However, handcrafting and selecting suitable statistics is a laborious task involving multiple trial-and-error steps. In this work, we introduce an active learning method for ABC statistics selection which reduces the domain expert’s work considerably. By involving the experts, we are able to handle misspecified models, unlike the existing dimension reduction methods. Moreover, empirical results show better posterior estimates than with existing methods, when the simulation budget is limited.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 39th International Conference on Machine Learning
KustantajaJMLR
Sivut1893-1905
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Machine Learning - Baltimore, Yhdysvallat
Kesto: 17 heinäk. 202223 heinäk. 2022
Konferenssinumero: 39

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaPMLR
Vuosikerta162
ISSN (elektroninen)2640-3498

Conference

ConferenceInternational Conference on Machine Learning
LyhennettäICML
Maa/AlueYhdysvallat
KaupunkiBaltimore
Ajanjakso17/07/202223/07/2022

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