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Sparse Hilbert Embedding-Based Statistical Inference of Stochastic Ecological Systems

  • Wilson González-Vanegas
  • Andrés Alvarez-Meza
  • Álvaro Orozco-Gutierrez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

The growth rate of a population has been an important aspect in ecological and biologic applications. Since a non-linear stochastic behavior is linked to this type of systems, the inference of the model parameters is a challenging task. Approximate Bayesian Computation (ABC) can be used for leading the intractability of the likelihood function caused by the model characteristics. Recently, some methods based on Hilbert Space Embedding (HSE) have been proposed in the context of ABC; nevertheless, the relevance of the observations and simulations are not contemplated. Here, we develop a Sparse HSE-based distance, termed SHSED, to compare distributions associated with two random variables through sparse estimations of the densities in a Reproducing Kernel Hilbert Space (RKHS). Namely, SHSED highlights relevant information using a sparse weighted representation of data within an ABC-based inference. Our method improves the inference accuracy of a Ricker map-based population model in comparison with other state-of-the-art ABC-based approaches.

Keywords

Hilbert space embeddings Ricker model Sparse methods Statistical inference 

Notes

Acknowledgments

Research under grants provided by the project “Desarrollo de una plataforma para el cálculo de confiabilidad en la operación interdependiente de los sistemas de gas natural y sector eléctrico de Colombia que permita evaluar alternativas de inversión y regulación para optimizar los costos de operación” with code: 1110-745-58696, funded by Colciencias. Moreover, author W. González-Vanegas was supported by Colciencias under the 706 agreement, “Jóvenes Investigadores e Innovadores-2015”.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Wilson González-Vanegas
    • 1
  • Andrés Alvarez-Meza
    • 1
  • Álvaro Orozco-Gutierrez
    • 1
  1. 1.Automatic Research Group, Faculty of EngineeringsUniversidad Tecnológica de PereiraPereiraColombia

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