An unsupervised classification method for point events occurring on a geometric network is
proposed. The idea relies on the distributional flexibility and practicality of random partition …

We consider the setting of multivariate functional data collected over time at each of a set of
sites. Our objective is to implement model-based clustering of the functions across the sites …

In this paper a mixture distribution model is extended to include spatial dependence of the
autoregressive type. The resulting model nests both spatial heterogeneity and spatial …

The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric
methods in regression and density estimation based on the spectral representation of …

We present the hierarchical Dirichlet scaling process (HDSP), a Bayesian nonparametric
mixed membership model for multi-labeled data. We construct the HDSP based on the …

We present a general construction for dependent random measures based on thinning
Poisson processes on an augmented space. The framework is not restricted to dependent …

We propose a space-time stick-breaking process for the disease cluster estimation. The
dependencies for spatial and temporal effects are introduced by using space-time covariate …

ABSTRACT Let (P 1,…, PJ) denote J populations of animals from distinct regions. A priori, it
is unknown which species are present in each region and what are their corresponding …

Covariate-dependent processes have been widely used in Bayesian nonparametric
statistics thanks to their flexibility to incorporate covariate information and correlation among …

The goal of this article is to develop a novel statistical model for studying cross-neuronal
spike train interactions during decision-making. For an individual to successfully complete …