Bayesian optimization for computationally extensive probability distributions.
An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training pha...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5837188?pdf=render |
| _version_ | 1811291504651534336 |
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| author | Ryo Tamura Koji Hukushima |
| author_facet | Ryo Tamura Koji Hukushima |
| author_sort | Ryo Tamura |
| collection | DOAJ |
| description | An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions. |
| first_indexed | 2024-04-13T04:30:26Z |
| format | Article |
| id | doaj.art-65a5765a42b04cf0b33622ec32155e15 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T04:30:26Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-65a5765a42b04cf0b33622ec32155e152022-12-22T03:02:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01133e019378510.1371/journal.pone.0193785Bayesian optimization for computationally extensive probability distributions.Ryo TamuraKoji HukushimaAn efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions.http://europepmc.org/articles/PMC5837188?pdf=render |
| spellingShingle | Ryo Tamura Koji Hukushima Bayesian optimization for computationally extensive probability distributions. PLoS ONE |
| title | Bayesian optimization for computationally extensive probability distributions. |
| title_full | Bayesian optimization for computationally extensive probability distributions. |
| title_fullStr | Bayesian optimization for computationally extensive probability distributions. |
| title_full_unstemmed | Bayesian optimization for computationally extensive probability distributions. |
| title_short | Bayesian optimization for computationally extensive probability distributions. |
| title_sort | bayesian optimization for computationally extensive probability distributions |
| url | http://europepmc.org/articles/PMC5837188?pdf=render |
| work_keys_str_mv | AT ryotamura bayesianoptimizationforcomputationallyextensiveprobabilitydistributions AT kojihukushima bayesianoptimizationforcomputationallyextensiveprobabilitydistributions |