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On predictive inference for intractable models via approximate Bayesian computation
Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based statistical models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility of ABC as a generic approximate method for pred...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911513/ https://www.ncbi.nlm.nih.gov/pubmed/36785730 http://dx.doi.org/10.1007/s11222-022-10163-6 |
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author | Järvenpää, Marko Corander, Jukka |
author_facet | Järvenpää, Marko Corander, Jukka |
author_sort | Järvenpää, Marko |
collection | PubMed |
description | Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based statistical models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility of ABC as a generic approximate method for predictive inference, in particular, for computing the posterior predictive distribution of future observations or missing data of interest. We consider three complementary ABC approaches for this goal, each based on different assumptions regarding which predictive density of the intractable model can be sampled from. The case where only simulation from the joint density of the observed and future data given the model parameters can be used for inference is given particular attention and it is shown that the ideal summary statistic in this setting is minimal predictive sufficient instead of merely minimal sufficient (in the ordinary sense). An ABC prediction approach that takes advantage of a certain latent variable representation is also investigated. We additionally show how common ABC sampling algorithms can be used in the predictive settings considered. Our main results are first illustrated by using simple time-series models that facilitate analytical treatment, and later by using two common intractable dynamic models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11222-022-10163-6. |
format | Online Article Text |
id | pubmed-9911513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-99115132023-02-11 On predictive inference for intractable models via approximate Bayesian computation Järvenpää, Marko Corander, Jukka Stat Comput OriginalPaper Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based statistical models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility of ABC as a generic approximate method for predictive inference, in particular, for computing the posterior predictive distribution of future observations or missing data of interest. We consider three complementary ABC approaches for this goal, each based on different assumptions regarding which predictive density of the intractable model can be sampled from. The case where only simulation from the joint density of the observed and future data given the model parameters can be used for inference is given particular attention and it is shown that the ideal summary statistic in this setting is minimal predictive sufficient instead of merely minimal sufficient (in the ordinary sense). An ABC prediction approach that takes advantage of a certain latent variable representation is also investigated. We additionally show how common ABC sampling algorithms can be used in the predictive settings considered. Our main results are first illustrated by using simple time-series models that facilitate analytical treatment, and later by using two common intractable dynamic models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11222-022-10163-6. Springer US 2023-02-09 2023 /pmc/articles/PMC9911513/ /pubmed/36785730 http://dx.doi.org/10.1007/s11222-022-10163-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | OriginalPaper Järvenpää, Marko Corander, Jukka On predictive inference for intractable models via approximate Bayesian computation |
title | On predictive inference for intractable models via approximate Bayesian computation |
title_full | On predictive inference for intractable models via approximate Bayesian computation |
title_fullStr | On predictive inference for intractable models via approximate Bayesian computation |
title_full_unstemmed | On predictive inference for intractable models via approximate Bayesian computation |
title_short | On predictive inference for intractable models via approximate Bayesian computation |
title_sort | on predictive inference for intractable models via approximate bayesian computation |
topic | OriginalPaper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911513/ https://www.ncbi.nlm.nih.gov/pubmed/36785730 http://dx.doi.org/10.1007/s11222-022-10163-6 |
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