| dc.contributor | 應數系 | |
| dc.creator (作者) | 邱普照 | |
| dc.creator (作者) | Kow, Pu-Zhao;Wang, Jenn-Nan | |
| dc.date (日期) | 2025-08 | |
| dc.date.accessioned | 24-Sep-2025 09:39:06 (UTC+8) | - |
| dc.date.available | 24-Sep-2025 09:39:06 (UTC+8) | - |
| dc.date.issued (上傳時間) | 24-Sep-2025 09:39:06 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/159634 | - |
| dc.description.abstract (摘要) | In this work, we consider the inverse problem of determining an unknown potential in a subdiffusion equation from its solution using a nonparametric Bayesian approach. Our aim is to establish the consistency of the posterior distribution with Gaussian priors. To do so, we need some key estimates of the forward problem. For the forward problem, we have to overcome the fact that the solution of the subdiffusion equation is less regular than that of the classical heat equation. The main ingredient is the maximum principle for the subdiffusion equation. We show that the posterior contracts to the ground truth at a polynomial rate. | |
| dc.format.extent | 98 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | SIAM/ASA Journal on Uncertainty Quantification, Vol.13, No.3, pp.1116-1144 | |
| dc.subject (關鍵詞) | inverse problem; Bayesian inference; Gaussian priors; contraction rate; Riemann–Liouville derivative; Caputo derivative | |
| dc.title (題名) | Consistency of Bayesian Inference for a Subdiffusion Equation | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1137/24M1707419 | |
| dc.doi.uri (DOI) | https://doi.org/10.1137/24M1707419 | |
