Spurred on by recent successes in causal inference competitions, Bayesian nonparametric
(and high‐dimensional) methods have recently seen increased attention in the causal …

Abstract Dirichlet Process Mixture (DPM) models have been increasingly employed to
specify random partition models that take into account possible patterns within covariates …

The foundation of the subject of nonparametric Bayesian inference was laid in two technical
reports: a 1969 UCLA report by Thomas S. Ferguson (later published in 1973 as a paper in …

Background The handling of missing data is a challenge for inference and regression
modelling. A particular challenge is dealing with missing predictor information, particularly …

We propose a general Bayesian nonparametric (BNP) approach to causal inference in the
point treatment setting. The joint distribution of the observed data (outcome, treatment, and …

Substantial advances in Bayesian methods for causal inference have been made in recent
years. We provide an introduction to Bayesian inference for causal effects for practicing …

Univariate or multivariate ordinal responses are often assumed to arise from a latent
continuous parametric distribution, with covariate effects that enter linearly. We introduce a …

We present a Bayesian nonparametric model for conditional distribution estimation using
Bayesian additive regression trees (BART). The generative model we use is based on …

Several epidemiological studies have provided evidence that long-term exposure to fine
particulate matter (pm2. 5) increases mortality rate. Furthermore, some population …

There is a growing interest in learning how the distribution of a response variable changes
with a set of observed predictors. Bayesian nonparametric dependent mixture models …