Mathematics in Zurich has a long and distinguished tradition, in which the writing of lecture
notes volumes and research monographs plays a prominent part. The Zurich Lectures in …

We study the problem of training diffusion models to sample from a distribution with a given
unnormalized density or energy function. We benchmark several diffusion-structured …

This paper aims at revisiting the classical results on Laplace approximation in a modern
nonasymptotic and dimension-free form. Such an extension is motivated by applications to …

We provide the rest of the proofs of the results contained in the main article, some technical
lemmas as well as additional simulations corroborating the theory. We also provide a …

This special issue is dedicated to Sir Adrian Smith, whose contributions to Bayesian analysis
have deeply impacted the field (or rather fields) of Bayesian inference, decision theory and …

We study the problem of training diffusion models to sample from a distribution with a given
unnormalized density or energy function. We benchmark several diffusion-structured …

The problem of efficiently generating random samples from high-dimensional and non-log-
concave posterior measures arising in nonlinear regression problems is considered …

Ensemble methods have become ubiquitous for the solution of Bayesian inference
problems. State-of-the-art Langevin samplers such as the ensemble Kalman sampler (EKS) …

We consider a class of infinite-dimensional dynamical systems driven by non-linear
parabolic partial differential equations with initial condition $\theta $ modelled by a Gaussian …

Our method proposes the efficient generation of samples from an unnormalized Boltzmann
density by solving the underlying continuity equation in the low-rank tensor train (TT) format …