Bayesian Inference of an Uncertain Generalized Diffusion Operator

DOI10.1137/21M141659XzbMath1481.62012arXiv2105.01807MaRDI QIDQ5862901

Teresa Portone, Robert D. Moser

Publication date: 10 March 2022

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.01807

zbMATH Keywords

anomalous diffusionBayesian inferenceoperator inferenceuncertain operator

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) PDEs in connection with fluid mechanics (35Q35) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32) Applications of operator theory in probability theory and statistics (47N30)

Related Items

Convergence Rates for Learning Linear Operators from Noisy Data

Uses Software

• SALib
• muq

Cites Work

• Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
• Simple marginally noninformative prior distributions for covariance matrices
• Making best use of model evaluations to compute sensitivity indices
• A physics-informed operator regression framework for extracting data-driven continuum models
• Data-driven deep learning of partial differential equations in modal space
• Data-driven operator inference for nonintrusive projection-based model reduction
• Modeling groundwater flow and contaminant transport
• Inverse problems: A Bayesian perspective
• Representing Model Inadequacy: A Stochastic Operator Approach
• Nonconjugate Bayesian Estimation of Covariance Matrices and Its Use in Hierarchical Models
• Multivariate Density Estimation
• Well-Posed Bayesian Inverse Problems: Priors with Exponential Tails
• On Information and Sufficiency
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates