We advocate an optimization-centric view of Bayesian inference. Our inspiration is the
representation of Bayes' rule as infinite-dimensional optimization (Csisz´ r, 1975; Donsker …

We advocate an optimization-centric view on and introduce a novel generalization of
Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite …

We present the GPry algorithm for fast Bayesian inference of general (non-Gaussian)
posteriors with a moderate number of parameters. GPry does not need any pre-training …

Efficiently accessing the information contained in non-linear and high dimensional
probability distributions remains a core challenge in modern statistics. Traditionally …

This paper introduces a generalized representation of Bayesian inference. It is derived
axiomatically, recovering existing Bayesian methods as special cases. We then use it to …

Bayesian inference has become a powerful and popular technique for understanding
psychological phenomena. However, compared with frequentist statistics, current methods …

Gaussian distributions are widely used in Bayesian variational inference to approximate
intractable posterior densities, but the ability to accommodate skewness can improve …

We study the variational inference problem of minimizing a regularized R\'enyi divergence
over an exponential family, and propose a relaxed moment-matching algorithm, which …

Background Many observational studies have shown a close association between gut
microbiota and the risk of various duodenal diseases. Therefore, we urgently explore the …

Health warning labels have been found to increase awareness of the harmful effects of
tobacco products. An eye tracking study was conducted to determine the optimal placement …