A Bayesian nonparametric model for binary random variables is introduced. The characterization of the probability model is based on the Dirichlet process and on the Poisson hyperplane tessellation model. These two stochastic models are combined in order to adapt, under the hypothesis of partial...

A Polya tree is characterised by a special class of predictive probabilities.

This paper introduces a bivariate Dirichlet process for modelling a partially exchangeable sequence of observables. The proposed model would be relevant when two distributions are unknown but are thought to be close to each other. For two random distributions with the same marginals, the belief...

Based on reinforced urn process introduced by Muliere et al. [2000. Urn schemes and reinforced random walks. Stochastic Process. Appl. 88(1), 59-78] we propose a Bayesian nonparametric approach to analyse a design determining the maximum tolerated dose (MTD) in Phase I clinical trials for new...

We define a reinforced stochastic process of random variables indexed by the vertices of a k-tree and with values in a Polish space. The work presents a natural extension from an exchangeable to a partially exchangeable setting of previous work done by the authors.

We introduce a stochastic process based on nonhomogeneous Poisson processes and urn processes which can be reinforced to produce a mixture of semi-Markov processes. By working with the notion of exchangeable blocks within the process, we present a Bayesian nonparametric framework for handling...