This work focuses on using Bayesian Additive Regression Trees (BART), a flexible and
computationally efficient regression method, to model heterogeneity in data. In particular, we …

We study Dirichlet process–based models for sets of predictor–dependent probability
distributions, where the domain and predictor space are general Polish spaces. We …

Bayesian nonparametric priors are distributions on functions. In this thesis, we present
several novel Bayesian approaches based on the elicitation of a set of nonparametric priors …

The assumption of group heterogeneity has become popular in panel data models. Instead
of modeling heterogeneity via unit-specific coefficients, the cross-sectional units are …

This thesis considers the direct connection between the prediction of future observations
and Bayesian inference. Using prediction as a guide, we generalize the Bayesian …

Aux alentours de son premier anniversaire, l'enfant humain prononce son premier mot.
Cette première production n'est pourtant pas le début de son apprentissage de la langue …

Inferring a partition of a dataset can help in downstream analyses and decision making.
However, there often exist many feasible partitions, which makes the problem of inferring …

Group heterogeneity has recently been introduced in the panel data model. We develop a
constrained Bayesian grouped estimator that effectively exploits researchers' prior belief on …

In recent years, intervention researchers have turned to mobile health (mHealth) technology
as a potential alternative to traditional tobacco cessation counseling, which smokers report …

Extreme value theory (EVT) provides theoretical foundation to assess the probability of rare
and extreme events. This thesis provides novel Bayesian semiparametric models for …