Bayesian nonparametric testing for embedded hypotheses with application to shape constrains If Bayesian nonparametric methods have received a great interest in the literature, only a few is known for testing nonparametric hypotheses, and especially the asymptotic properties of such tests. The problem of testing between two nonparametric hypotheses is known to be difficult, but the…

Spatial gamma processes in disease mapping In this talk we will present Bayesian models based on Markov random fields of gamma type to model the relative risk in disease mapping data. The spatial gamma processes allow for different spatial dependence among neighbours. We describe the properties of ths processes and use them as prior distributions…

Dependent species sampling models We consider a novel Bayesian nonparametric model for density estimation with an underlying spatial structure. The model is built on a class of species sampling models, which are discrete random probability measures that can be represented as a mixture of random support points and random weights. Specifically, we construct a collection…

Generalized partially additive Bayesian spectral analysis regression models In this talk, we present a Bayesian method for generalized partially additive regression using a spectral analysis of Gaussian process priors  for the  regression function. The smoothing prior distribution for the spectral coefficients incorporates hyper parameters that control the smoothness of the function and the tradeoff between…

False discovery rate smoothing Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis.  But this may be inappropriate for many of today's large-scale screening problems, where test statistics have a natural spatial lattice structure (voxels in the brain, distance along…