The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we
present a different perspective that focuses on missing observations as the source of …

Statisticians often face the choice between using probability models or a paradigm defined
by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into …

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem
Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by …

The area under the receiver operating characteristic curve (AUC) serves as a summary of a
binary classifier's performance. For inference on the AUC, a common modeling assumption …

Bayesian nonparametric methods are a popular choice for analysing survival data due to
their ability to flexibly model the distribution of survival times. These methods typically …

The number of always-online vehicles continuously increases, and these vehicles will form
an immense mobile sensor network. For example, cars can upload live temperature and …

Current tools for multivariate density estimation struggle when the density is concentrated
near a non-linear subspace or manifold. Most approaches require the choice of a kernel …

Spectral clustering views the similarity matrix as a weighted graph, and partitions the data by
minimizing a graph-cut loss. Since it minimizes the across-cluster similarity, there is no need …

Predictive recursion (PR) is a fast, recursive algorithm that gives a smooth estimate of the
mixing distribution under the general mixture model. However, the PR algorithm requires …

We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian
mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform …