Particle filters contain the promise of fully nonlinear data assimilation. They have been
applied in numerous science areas, including the geosciences, but their application to high …

Solving inverse problems without the use of derivatives or adjoints of the forward model is
highly desirable in many applications arising in science and engineering. In this paper we …

Fokker–Planck equations are extensively employed in various scientific fields as they
characterise the behaviour of stochastic systems at the level of probability density functions …

Bayesian inference problems require sampling or approximating high-dimensional
probability distributions. The focus of this paper is on the recently introduced Stein …

We propose a computational method (with acronym ALDI) for sampling from a given target
distribution based on first-order (overdamped) Langevin dynamics which satisfies the …

Data assimilation addresses the general problem of how to combine model-based
predictions with partial and noisy observations of the process in an optimal manner. This …

Bayesian inference can be embedded into an appropriately defined dynamics in the space
of probability measures. In this paper, we take Brownian motion and its associated Fokker …

In this paper, we study efficient approximate sampling for probability distributions known up
to normalization constants. We specifically focus on a problem class arising in Bayesian …

The increasing availability of data presents an opportunity to calibrate unknown parameters
which appear in complex models of phenomena in the biomedical, physical, and social …

We propose a projected Wasserstein gradient descent method (pWGD) for high-dimensional
Bayesian inference problems. The underlying density function of a particle system of …