This paper provides a unifying mean field based framework for the derivation and analysis of
ensemble Kalman methods. Both state estimation and parameter estimation problems are …

We introduce $\textit {Stein transport} $, a novel methodology for Bayesian inference
designed to efficiently push an ensemble of particles along a predefined curve of tempered …

Various particle filters have been proposed over the last couple of decades with the common
feature that the update step is governed by a type of control law. This feature makes them an …

In this paper, we describe two effects of the Lévy area correction on the invariant measure of
stochastic rigid body dynamics on geometric rough paths. From the viewpoint of dynamics …

Kalman filters constitute a scalable and robust methodology for approximate Bayesian
inference, matching first and second order moments of the target posterior. To improve the …

We present a novel methodology based on filtered data and moving averages for estimating
effective dynamics from observations of multiscale systems. We show in a semi-parametric …

Standard maximum likelihood or Bayesian approaches to parameter estimation for
stochastic differential equations are not robust to perturbations in the continuous-in-time …

We present a new version of the stochastic sewing lemma, capable of handling multiple
discontinuous control functions. This is then used to develop a theory of rough stochastic …

In this work we show that rough stochastic differential equations (RSDEs), as introduced by
Friz, Hocquet, and L\^ e (2021), are Malliavin differentiable. We use this to prove existence …

This paper is concerned with the derivation and mathematical analysis of continuous time
Ensemble Kalman Filters (EnKBFs) and related data assimilation methods for Stochastic …