We propose a nonparametric compound Poisson model for underreported count data that
introduces a latent clustering structure for the reporting probabilities. The latter are estimated …

Several extensions of the familiar Dirichlet process have been widely investigated to
nonparametric Bayesian model fittings parallel with appealing subsequent studies on their …

The availability of complex-structured data has sparked new research directions in statistics
and machine learning. Bayesian nonparametrics is at the forefront of this trend thanks to two …

We propose Dirichlet process mixture (DPM) models for prediction and cluster‐wise variable
selection, based on two choices of shrinkage baseline prior distributions for the linear …

Overview of notation and models, details of the MCMC algorithms, description of the model
by Sun et al.(2014), empirical results for the Bayes factor approach, and MCMC trace plots …

We develop a nonparametric Bayesian modeling approach to ordinal regression based on
priors placed directly on the discrete distribution of the ordinal responses. The prior …

We propose a methodology for modelling and comparing probability distributions within a
Bayesian nonparametric framework. Building on dependent normalised random measures …

We propose a Bayesian hypothesis testing procedure for comparing the multivariate
distributions of several treatment groups against a control group. This test is derived from a …

Motivated by an increasing demand for models that can effectively describe features of
complex multivariate time series, eg from sensor data in biomechanics, motion analysis, and …

One core assumption typically adopted for valid causal inference is that of no interference
between experimental units, ie, the outcome of an experimental unit is unaffected by the …