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An Automatic Approximate Bayesian Computation Approach Using Metric Learning

  • W. González-VanegasEmail author
  • A. Álvarez-Meza
  • A. Orozco-Gutiérrez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Recent progress in Bayesian inference has allowed for accurate posterior estimations in complex situations with no idea about a likelihood function. Currently, Approximate Bayesian Computation (ABC) techniques have emerged as a widely used set of free-likelihood methods. Although there is a large number of different ABC-based approaches across the literature, all they have in common a hard dependence on free parameters selection, demanding for expensive tuning procedures such as grid search or cross-validation. Here, we introduce an Automatic Metric Learning-based ABC approach, termed AML-ABC. Namely, AML-ABC matches the simulation and observation spaces within an ABC-based framework. Attained results on a synthetic dataset and a real-world ecological system show that our approach is a competitive method compared to other non-automatic state-of-the-art ABC techniques.

Keywords

Approximate Bayesian Computation Kernel methods Metric learning Non-linear dynamic system Statistical inference 

Notes

Acknowledgments

Research under grants provided by the project 1110-745-58696, funded by Colciencias, Colombia. Authors would like to thank the Master in Electrical Engineering program from Universidad Tecnológica de Pereira for partially funding this research. Moreover, author W. González-Vanegas was supported under the project E6-18-2, funded by Universidad Tecnológica de Pereira.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • W. González-Vanegas
    • 1
    Email author
  • A. Álvarez-Meza
    • 1
  • A. Orozco-Gutiérrez
    • 1
  1. 1.Faculty of Engineerings, Automatic Research GroupUniversidad Tecnológica de PereiraPereiraColombia

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