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 particularly …

Abstract System identification of dynamical systems is often posed as a least squares
minimization problem. The aim of these optimization problems is typically to learn either …

This paper introduces a computational framework to reconstruct and forecast a partially
observed state that evolves according to an unknown or expensive-to-simulate dynamical …

This work aims to analyze the specific application of sensor environment perception based
on the Kalman filter algorithm in intelligent vehicles. Hence, this work proposes a design for …

Approximate Bayesian computation (ABC) is the most popular approach to inferring
parameters in the case where the data model is specified in the form of a simulator. It is not …

This study presents a sparse grid interpolation and ensemble Kalman filter (EnKF)‐based
Markov Chain Monte Carlo (MCMC) method (SG‐EnMCMC). Initiating with the formulation of …

Abstract Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the
approximate solution of Bayesian inverse problems with expensive forward models …

In this article we consider the estimation of the log-normalization constant associated to a
class of continuous-time filtering models. In particular, we consider ensemble Kalman–Bucy …

Data assimilation (DA) is a key procedure that synchronizes a computer model with real
observations. However, in the case of overparametrized complex systems modeling, the task …

This paper proposes a probabilistic Bayesian formulation for system identification (ID) and
estimation of nonseparable Hamiltonian systems using stochastic dynamic models …