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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.
|Title of host publication||Proceedings of the 39th International Conference on Machine Learning|
|Publication status||Published - 2022|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Machine Learning - Baltimore, United States|
Duration: 17 Jul 2022 → 23 Jul 2022
Conference number: 39
|Name||Proceedings of Machine Learning Research|
|Conference||International Conference on Machine Learning|
|Period||17/07/2022 → 23/07/2022|
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- 1 Active
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding