Many recent applications of nonparametric Bayesian inference use random partition models, i.e. probability models for clustering a set of experimental units. We review the popular basic constructions. We then focus on an ...

Bayesian nonparametric models provide a general framework for flexible statistical modeling of modern complex data sets. We compare a rate-optimal and rate-suboptimal Bayesian nonparametric model for density estimation for ...

We discuss inference for data with repeated measurements at multiple levels. The motivating example is data with blood counts from cancer patients undergoing multiple cycles of chemotherapy, with days nested within cycles. ...

We propose Bayesian methods for selecting and estimating di erent types of mixture models which are widely used in Genetics and Molecular Biology. We speci cally propose data-driven selection and estimation methods for a ...

The problem we consider is how to assess the order of serial dependence within partially exchangeable binary sequences, We obtain exact conditional tests comparing any two orders by finding the conditional distribution of ...

We propose a probability model for random partitions in the presence of covariates. In other words, we develop a model-based clustering algorithm that exploits available covariates. The motivating application is predicting ...

We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In ...

We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times and the ...

The current advances of technology provides, among other things, several ways of collecting data, which enlarges the possibility of studying new phenomena. Researches focused on studying the functional relation between ...