We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (2007). This sampler allows the fitting of infinite mixture models with a wide–range of prior specification. To illustrate this flexiblity we develop a new nonparametric prior for...

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a...

The quantitative study of marked individuals relies mainly on the use of meaningful biological models. Classical inference is then conducted based on the model likelihood, parameterized by parameters such as survival, recovery, transition and recapture probabilities. In classical statistics, we...