We provide additional supporting plots and tables that show 1. Descriptive statistics of our
data. 2. The Estimated Intensity Functions on the real line. 3. Spatial plot showing …

Bayesian nonparametrics is a lively topic in the statistical literature. Thanks to its versatility,
the approach applies to a wide range of modern applications, from machine learning to …

In Bayesian nonparametrics, the specification of suitable (for practical purposes) stochastic
processes whose realisations are discrete probability measures plays a crucial role …

There is an increasing focus in several fields on learning how the distribution of a response
variable changes with a set of predictors. Bayesian nonparametric dependent mixture …

I metodi bayesiani non parametrici stanno diventando un vero e proprio paradigma
nell'analisi di dati complessi grazie alla loro estrema flessibilità. A differenza però dei metodi …

As the traditional machine learning setting assumes that the data are identically and
independently distributed (iid), this is quite like a perfect conditioned vacuum and seldom a …

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

This thesis presents a new framework for constituting a group of dependent completely
random measures, unifying and extending methods in the literature. The dependent …

Dirichlet process mixture (DPM) model is one of the most important Bayesian nonparametric
models owing to its efficiency of inference and flexibility for various applications. A …

Spatially constrained Dirichlet process mixture models are springing up in image processing
in recent years. However, inference for the model is NP-hard. Gibbs sampling which is a …