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Open AccessArticle

AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning

1
Automatics Research Group, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
2
Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(10), 932; https://doi.org/10.3390/e21100932
Received: 3 August 2019 / Revised: 10 September 2019 / Accepted: 19 September 2019 / Published: 24 September 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
Bayesian statistical inference under unknown or hard to asses likelihood functions is a very challenging task. Currently, approximate Bayesian computation (ABC) techniques have emerged as a widely used set of likelihood-free methods. A vast number of ABC-based approaches have appeared in the literature; however, they all share a hard dependence on free parameters selection, demanding expensive tuning procedures. In this paper, we introduce an automatic kernel learning-based ABC approach, termed AKL-ABC, to automatically compute posterior estimations from a weighting-based inference. To reach this goal, we propose a kernel learning stage to code similarities between simulation and parameter spaces using a centered kernel alignment (CKA) that is automated via an Information theoretic learning approach. Besides, a local neighborhood selection (LNS) algorithm is used to highlight local dependencies over simulations relying on graph theory. Attained results on synthetic and real-world datasets show our approach is a quite competitive method compared to other non-automatic state-of-the-art ABC techniques. View Full-Text
Keywords: approximate Bayesian computation; graph theory; kernel learning; non-linear dynamic system; statistical inference approximate Bayesian computation; graph theory; kernel learning; non-linear dynamic system; statistical inference
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González-Vanegas, W.; Álvarez-Meza, A.; Hernández-Muriel, J.; Orozco-Gutiérrez, Á. AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning. Entropy 2019, 21, 932.

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