8 citations · 19 across the 7 of their papers we have counts for
12 papers
Hierarchical Ensemble Kalman Methods with Sparsity-Promoting Generalized Gamma Hyperpriors
Hwanwoo Kim, Daniel Sanz-Alonso, Alexander Strang
This paper introduces a computational framework to incorporate flexible regularization techniques in ensemble Kalman methods for nonlinear inverse problems. The proposed methodolog…
Auto-differentiable Ensemble Kalman Filters
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is part…
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…
Bayesian Update with Importance Sampling: Required Sample Size
Daniel Sanz-Alonso, Zijian Wang
Importance sampling is used to approximate Bayes' rule in many computational approaches to Bayesian inverse problems, data assimilation and machine learning. This paper reviews and…
Data-Driven Forward Discretizations for Bayesian Inversion
Daniele Bigoni, Yuming Chen, Nicolas Garcia Trillos +2
This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governi…
HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate
M. P. Calvo, D. Sanz-Alonso, J. M. Sanz-Serna
The leapfrog integrator is routinely used within the Hamiltonian Monte Carlo method and its variants. We give strong numerical evidence that alternative, easy to implement algorith…