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    政大機構典藏 > 理學院 > 應用數學系 > 期刊論文 >  Item 140.119/159634
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/159634


    Title: Consistency of Bayesian Inference for a Subdiffusion Equation
    Authors: 邱普照
    Kow, Pu-Zhao;Wang, Jenn-Nan
    Contributors: 應數系
    Keywords: inverse problem;Bayesian inference;Gaussian priors;contraction rate;Riemann–Liouville derivative;Caputo derivative
    Date: 2025-08
    Issue Date: 2025-09-24 09:39:06 (UTC+8)
    Abstract: 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.
    Relation: SIAM/ASA Journal on Uncertainty Quantification, Vol.13, No.3, pp.1116-1144
    Data Type: article
    DOI 連結: https://doi.org/10.1137/24M1707419
    DOI: 10.1137/24M1707419
    Appears in Collections:[應用數學系] 期刊論文

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