Approximate Bayesian Computation with Domain Expert in the Loop

Ayush Bharti, Louis Filstroff, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 39th International Conference on Machine Learning
PublisherJournal of Machine Learning Research
Pages1893-1905
Number of pages13
Publication statusPublished - 20 Jun 2022
EventInternational Conference on Machine Learning -
Duration: 17 Jul 202223 Jul 2022

Conference

ConferenceInternational Conference on Machine Learning
Period17/07/2223/07/22

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI
  • Digital Futures
  • Sustainable Futures

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