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This paper is a note on the use of Bayesian nonparametric mixture models for continuous time series. We identify a key requirement for such models, and then establish that there is a single type of mo...
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine vision, image analysis and many others. When analyzing such data, parametric models are often to...
We present an alternative approach to the Bayesian nonparametric analysis of conditional species richness under two-parameter Poisson Dirichlet priors. We rely on a known characteri- zation by delet...
We derive explicit Bayesian nonparametric analysis for a species sampling model with finitely many types of Gibbs form of type = −1 recently introduced in Gnedin (2009). Our results compleme...
The process of calibrating radiocarbon determinations onto the cal- endar scale involves, as a rst stage, the estimation of the relationship between calendar and radiocarbon ages (the radiocarbon ca...

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