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 …

Bayesian inference provides a consistent method for the extraction of information from
physics experiments even in ill-conditioned circumstances. The approach provides a unified …

We introduce a simple new approach to variable selection in linear regression, with a
particular focus on quantifying uncertainty in which variables should be selected. The …

Scene representation—the process of converting visual sensory data into concise
descriptions—is a requirement for intelligent behavior. Recent work has shown that neural …

Multi‐omics studies promise the improved characterization of biological processes across
molecular layers. However, methods for the unsupervised integration of the resulting …

We introduce a new, efficient, principled and backpropagation-compatible algorithm for
learning a probability distribution on the weights of a neural network, called Bayes by …

By providing a simple and efficient way of computing low-variance gradients of continuous
random variables, the reparameterization trick has become the technique of choice for …

A useful definition of 'big data'is data that is too big to process comfortably on a single
machine, either because of processor, memory, or disk bottlenecks. Graphics processing …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

Information theory and inference, often taught separately, are here united in one entertaining
textbook. These topics lie at the heart of many exciting areas of contemporary science and …