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Prepaid parameter estimation without likelihoods
In various fields, statistical models of interest are analytically intractable and inference is usually performed using a simulation-based method. However elegant these methods are, they are often painstakingly slow and convergence is difficult to assess. As a result, statistical inference is greatl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752867/ https://www.ncbi.nlm.nih.gov/pubmed/31498789 http://dx.doi.org/10.1371/journal.pcbi.1007181 |
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author | Mestdagh, Merijn Verdonck, Stijn Meers, Kristof Loossens, Tim Tuerlinckx, Francis |
author_facet | Mestdagh, Merijn Verdonck, Stijn Meers, Kristof Loossens, Tim Tuerlinckx, Francis |
author_sort | Mestdagh, Merijn |
collection | PubMed |
description | In various fields, statistical models of interest are analytically intractable and inference is usually performed using a simulation-based method. However elegant these methods are, they are often painstakingly slow and convergence is difficult to assess. As a result, statistical inference is greatly hampered by computational constraints. However, for a given statistical model, different users, even with different data, are likely to perform similar computations. Computations done by one user are potentially useful for other users with different data sets. We propose a pooling of resources across researchers to capitalize on this. More specifically, we preemptively chart out the entire space of possible model outcomes in a prepaid database. Using advanced interpolation techniques, any individual estimation problem can now be solved on the spot. The prepaid method can easily accommodate different priors as well as constraints on the parameters. We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service. Our method outperforms state-of-the-art estimation techniques in both speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to handle previously quasi inestimable models. |
format | Online Article Text |
id | pubmed-6752867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67528672019-09-27 Prepaid parameter estimation without likelihoods Mestdagh, Merijn Verdonck, Stijn Meers, Kristof Loossens, Tim Tuerlinckx, Francis PLoS Comput Biol Research Article In various fields, statistical models of interest are analytically intractable and inference is usually performed using a simulation-based method. However elegant these methods are, they are often painstakingly slow and convergence is difficult to assess. As a result, statistical inference is greatly hampered by computational constraints. However, for a given statistical model, different users, even with different data, are likely to perform similar computations. Computations done by one user are potentially useful for other users with different data sets. We propose a pooling of resources across researchers to capitalize on this. More specifically, we preemptively chart out the entire space of possible model outcomes in a prepaid database. Using advanced interpolation techniques, any individual estimation problem can now be solved on the spot. The prepaid method can easily accommodate different priors as well as constraints on the parameters. We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service. Our method outperforms state-of-the-art estimation techniques in both speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to handle previously quasi inestimable models. Public Library of Science 2019-09-09 /pmc/articles/PMC6752867/ /pubmed/31498789 http://dx.doi.org/10.1371/journal.pcbi.1007181 Text en © 2019 Mestdagh et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Mestdagh, Merijn Verdonck, Stijn Meers, Kristof Loossens, Tim Tuerlinckx, Francis Prepaid parameter estimation without likelihoods |
title | Prepaid parameter estimation without likelihoods |
title_full | Prepaid parameter estimation without likelihoods |
title_fullStr | Prepaid parameter estimation without likelihoods |
title_full_unstemmed | Prepaid parameter estimation without likelihoods |
title_short | Prepaid parameter estimation without likelihoods |
title_sort | prepaid parameter estimation without likelihoods |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752867/ https://www.ncbi.nlm.nih.gov/pubmed/31498789 http://dx.doi.org/10.1371/journal.pcbi.1007181 |
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