Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024240 |
Incorporating Posterior-Informed Approximation Errors Into a Hierarchical Framework to Facilitate Out-of-the-BoxMCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification | |
Maclaren, Oliver J.; 39;Sullivan, John P.; 39;Sullivan, Michael J. | |
2020 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:1 |
文章类型 | Article |
语种 | 英语 |
国家 | New Zealand |
英文摘要 | We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our main goal is to make standard, "out-of-the-box" Markov chain Monte Carlo (MCMC) sampling more feasible for complex simulation models by using suitable approximations. To do this, we first show how to pose both the inverse and prediction problems in a hierarchical Bayesian framework. We then show how to incorporate so-called posterior-informed model approximation error into this hierarchical framework, using a modified form of the Bayesian approximation error approach. This enables the use of a "coarse," approximate model in place of a finer, more expensive model, while accounting for the additional uncertainty and potential bias that this can introduce. Our method requires only simple probability modeling, a relatively small number of fine model simulations and only modifies the target posterior-any standard MCMC sampling algorithm can be used to sample the new posterior. These corrections can also be used in methods that are not based on MCMC sampling. We show that our approach can achieve significant computational speedups on two geothermal test problems. We also demonstrate the dangers of naively using coarse, approximate models in place of finer models, without accounting for the induced approximation errors. The naive approach tends to give overly confident and biased posteriors while incorporating Bayesian approximation error into our hierarchical framework corrects for this while maintaining computational efficiency and ease of use. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000520132500033 |
WOS关键词 | CHAIN MONTE-CARLO ; BAYESIAN CALIBRATION ; NATURAL STATE ; MODELS ; MCMC |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/280474 |
专题 | 资源环境科学 |
作者单位 | Univ Auckland, Dept Engn Sci, Auckland, New Zealand |
推荐引用方式 GB/T 7714 | Maclaren, Oliver J.,39;Sullivan, John P.,39;Sullivan, Michael J.. Incorporating Posterior-Informed Approximation Errors Into a Hierarchical Framework to Facilitate Out-of-the-BoxMCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification[J]. WATER RESOURCES RESEARCH,2020,56(1). |
APA | Maclaren, Oliver J.,39;Sullivan, John P.,&39;Sullivan, Michael J..(2020).Incorporating Posterior-Informed Approximation Errors Into a Hierarchical Framework to Facilitate Out-of-the-BoxMCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification.WATER RESOURCES RESEARCH,56(1). |
MLA | Maclaren, Oliver J.,et al."Incorporating Posterior-Informed Approximation Errors Into a Hierarchical Framework to Facilitate Out-of-the-BoxMCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification".WATER RESOURCES RESEARCH 56.1(2020). |
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