Kinetic models are critical to predict the dynamic behaviour of metabolic networks.
Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting …

In this survey, we describe controlled interacting particle systems (CIPS) to approximate the
solution of the optimal filtering and the optimal control problems. Part I of the survey is …

The discovery of particle filtering methods has enabled the use of nonlinear filtering in a
wide array of applications. Unfortunately, the approximation error of particle filters typically …

We investigate the stability of a Sequential Monte Carlo (SMC) method applied to the
problem of sampling from a target distribution on R^d for large d. It is well known Bengtsson …

In modern applications, statisticians are faced with integrating heterogeneous data
modalities relevant for an inference, prediction, or decision problem. In such circumstances …

We consider the numerical approximation of the filtering problem in high dimensions, that is,
when the hidden state lies in ℝd with large d. For low-dimensional problems, one of the most …

We consider the inverse problem of estimating the initial condition of a partial differential
equation, which is observed only through noisy measurements at discrete time intervals. In …

Kinetic models are essential to quantitatively understand and predict the behaviour of
metabolic networks. Detailed and thermodynamically feasible kinetic models of metabolism …

This paper presents a variational representation of the Bayes' law using optimal
transportation theory. The variational representation is in terms of the optimal transportation …

In this work, we combine a stochastic model reduction with a particle filter augmented with
tempering and jittering, and apply the combined algorithm to a damped and forced …