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20172026
most citedParameterizations for Ensemble Kalman Inversion

83 citations · 126 across the 30 of their papers we have counts for

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7 papers · 1 filter

stat.ML2022★ 1 cited

A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators

Neil K. Chada, Quanjun Lang, Fei Lu +1

Kernels are efficient in representing nonlocal dependence and they are widely used to design operators between function spaces. Thus, learning kernels in operators from data is an…

stat.CO2022

Bayesian inversion with α-stable priors

Jarkko Suuronen, Tomás Soto, Neil K. Chada +1

We propose to use Lévy α-stable distributions for constructing priors for Bayesian inverse problems. The construction is based on Markov fields with stable-distributed increments.…

stat.ME2022

Unbiased Estimation of the Vanilla and Deterministic Ensemble Kalman-Bucy Filters

Miguel Alvarez, Neil K. Chada, Ajay Jasra

In this article we consider the development of an unbiased estimator for the ensemble Kalman--Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology which can be…

math.NA2022

On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data

Neil K. Chada, Marco A. Iglesias, Shuai Lu +1

For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown quantities. While most…

math.NA2022

A Review of the EnKF for Parameter Estimation

Neil K. Chada

The ensemble Kalman filter is a well-known and celebrated data assimilation algorithm. It is of particular relevance as it used for high-dimensional problems, by updating an ensemb…

stat.CO2022

Unbiased Estimation using Underdamped Langevin Dynamics

Hamza Ruzayqat, Neil K. Chada, Ajay Jasra

In this work we consider the unbiased estimation of expectations w.r.t.~probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normali…

stat.CO2022

Bayesian Deep Learning with Multilevel Trace-class Neural Networks

Neil K. Chada, Ajay Jasra, Kody J. H. Law +1

In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which can be preferable to tradi…