In many statistical applications that concern mathematical psychologists, the concept of
Fisher information plays an important role. In this tutorial we clarify the concept of Fisher …

ABSTRACT A key challenge for modern Bayesian statistics is how to perform scalable
inference of posterior distributions. To address this challenge, variational Bayes (VB) …

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 …

In this paper, I review the main results on the asymptotic properties of the posterior
distribution in nonparametric or high-dimensional models. In particular, I explain how …

We investigate the frequentist coverage of Bayesian credible sets in a nonparametric setting.
We consider a scale of priors of varying regularity and choose the regularity by an empirical …

Bernstein–von Mises theorems for nonparametric Bayes priors in the Gaussian white noise
model are proved. It is demonstrated how such results justify Bayes methods as efficient …

We continue the investigation of Bernstein–von Mises theorems for nonparametric Bayes
procedures from [Ann. Statist. 41 (2013) 1999–2028]. We introduce multiscale spaces on …

A Bernstein-von Mises theorem for smooth functionals in semiparametric models Page 1 The
Annals of Statistics 2015, Vol. 43, No. 6, 2353–2383 DOI: 10.1214/15-AOS1336 © Institute of …

In a smooth semiparametric estimation problem, the marginal posterior for the parameter of
interest is expected to be asymptotically normal and satisfy frequentist criteria of optimality if …

Supplement to “Nonparametric statistical inference for drift vector fields of multi-dimensional
diffusions”. The remaining proofs and technical results, along with a review of the relevant …