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AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning
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 literat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514265/ http://dx.doi.org/10.3390/e21100932 |
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author | González-Vanegas, Wilson Álvarez-Meza, Andrés Hernández-Muriel, José Orozco-Gutiérrez, Álvaro |
author_facet | González-Vanegas, Wilson Álvarez-Meza, Andrés Hernández-Muriel, José Orozco-Gutiérrez, Álvaro |
author_sort | González-Vanegas, Wilson |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7514265 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75142652020-11-09 AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning González-Vanegas, Wilson Álvarez-Meza, Andrés Hernández-Muriel, José Orozco-Gutiérrez, Álvaro Entropy (Basel) Article 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. MDPI 2019-09-24 /pmc/articles/PMC7514265/ http://dx.doi.org/10.3390/e21100932 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article González-Vanegas, Wilson Álvarez-Meza, Andrés Hernández-Muriel, José Orozco-Gutiérrez, Álvaro AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning |
title | AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning |
title_full | AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning |
title_fullStr | AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning |
title_full_unstemmed | AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning |
title_short | AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning |
title_sort | akl-abc: an automatic approximate bayesian computation approach based on kernel learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514265/ http://dx.doi.org/10.3390/e21100932 |
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