One of the core problems of modern statistics is to approximate difficult-to-compute
probability densities. This problem is especially important in Bayesian statistics, which …

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

Local convexity of the TAP free energy and AMP convergence for Z2-synchronization Page 1
The Annals of Statistics 2023, Vol. 51, No. 2, 519–546 https://doi.org/10.1214/23-AOS2257 © …

Joint Species Distribution Models (JSDM) provide a general multivariate framework to study
the joint abundances of all species from a community. JSDM account for both structuring …

Variational inference (VI) provides an appealing alternative to traditional sampling-based
approaches for implementing Bayesian inference due to its conceptual simplicity, statistical …

In this paper we consider generalized linear latent variable models that can handle
overdispersed counts and continuous but non-negative data. Such data are common in …

Many application domains, such as ecology or genomics, have to deal with multivariate non-
Gaussian observations. A typical example is the joint observation of the respective …

Internet recommender systems are popular in contexts that include heterogeneous
consumers and numerous products. In such contexts, product features that adequately …

Variational Bayes (VB) is a scalable alternative to Markov chain Monte Carlo (MCMC) for
Bayesian posterior inference. Though popular, VB comes with few theoretical guarantees …

Variational approximation methods have become a mainstay of contemporary machine
learning methodology, but currently have little presence in statistics. We devise an effective …