We present a tutorial on Bayesian optimization, a method of finding the maximum of
expensive cost functions. Bayesian optimization employs the Bayesian technique of setting …

The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

In this article, we introduce the BART R package which is an acronym for Bayesian additive
regression trees. BART is a Bayesian nonparametric, machine learning, ensemble …

Statistical models of text have become increasingly popular in statistics and computer
science as a method of exploring large document collections. Social scientists often want to …

In this paper, we study a method to sample from a target distribution π over R^d having a
positive density with respect to the Lebesgue measure, known up to a normalisation factor …

Sampling from various kinds of distribution is an issue of paramount importance in statistics
since it is often the key ingredient for constructing estimators, test procedures or confidence …

We propose a new data-augmentation strategy for fully Bayesian inference in models with
binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions …

High-dimensional Bayesian inference via the unadjusted Langevin algorithm Page 1
Bernoulli 25(4A), 2019, 2854–2882 https://doi.org/10.3150/18-BEJ1073 High-dimensional …

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

In this paper, we provide new insights on the Unadjusted Langevin Algorithm. We show that
this method can be formulated as the first order optimization algorithm for an objective …