32 citations · 183 across the 54 of their papers we have counts for
6 papers · 1 filter
Time-uniform accuracy of ensemble Kalman filters with localization
Xiaoou Cheng, Daniel Sanz-Alonso, Nathan Waniorek
This paper establishes time-uniform accuracy guarantees for deterministic square-root ensemble Kalman filters in linear--Gaussian state-space models under perfect-model dynamics. F…
Gaussian Process Regression under Computational and Epistemic Misspecification
Daniel Sanz-Alonso, Ruiyi Yang
Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-ra…
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement
Daniel Sanz-Alonso, Nathan Waniorek
This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional…
Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries
John Harlim, Shixiao Jiang, Hwanwoo Kim +1
This paper develops manifold learning techniques for the numerical solution of PDE-constrained Bayesian inverse problems on manifolds with boundaries. We introduce graphical Matérn…
Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants
Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso
This paper provides a unified perspective of iterative ensemble Kalman methods, a family of derivative-free algorithms for parameter reconstruction and other related tasks. We iden…
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds
John Harlim, Daniel Sanz-Alonso, Ruiyi Yang
This paper investigates the formulation and implementation of Bayesian inverse problems to learn input parameters of partial differential equations (PDEs) defined on manifolds. Spe…