Cargando…
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inferenc...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956883/ https://www.ncbi.nlm.nih.gov/pubmed/31997856 http://dx.doi.org/10.1007/s11222-017-9738-6 |
_version_ | 1783487219794706432 |
---|---|
author | Gutmann, Michael U. Dutta, Ritabrata Kaski, Samuel Corander, Jukka |
author_facet | Gutmann, Michael U. Dutta, Ritabrata Kaski, Samuel Corander, Jukka |
author_sort | Gutmann, Michael U. |
collection | PubMed |
description | Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11222-017-9738-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6956883 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-69568832020-01-27 Likelihood-free inference via classification Gutmann, Michael U. Dutta, Ritabrata Kaski, Samuel Corander, Jukka Stat Comput Article Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11222-017-9738-6) contains supplementary material, which is available to authorized users. Springer US 2017-03-13 2018 /pmc/articles/PMC6956883/ /pubmed/31997856 http://dx.doi.org/10.1007/s11222-017-9738-6 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Gutmann, Michael U. Dutta, Ritabrata Kaski, Samuel Corander, Jukka Likelihood-free inference via classification |
title | Likelihood-free inference via classification |
title_full | Likelihood-free inference via classification |
title_fullStr | Likelihood-free inference via classification |
title_full_unstemmed | Likelihood-free inference via classification |
title_short | Likelihood-free inference via classification |
title_sort | likelihood-free inference via classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956883/ https://www.ncbi.nlm.nih.gov/pubmed/31997856 http://dx.doi.org/10.1007/s11222-017-9738-6 |
work_keys_str_mv | AT gutmannmichaelu likelihoodfreeinferenceviaclassification AT duttaritabrata likelihoodfreeinferenceviaclassification AT kaskisamuel likelihoodfreeinferenceviaclassification AT coranderjukka likelihoodfreeinferenceviaclassification |