Agostino Nobile

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A new Markov chain Monte Carlo method for the Bayesian analysis of finite mixture distributions with an unknown number of components is presented. The sampler is characterized by a state space consisting only of the number of components and the latent allocation variables. Its main advantage is that it can be used, with minimal changes, for mixtures of(More)
Dual-channel microarray experiments can be regarded as designs with block-size two. Our aim is to find D-optimal, A-optimal and related L-optimal designs for such experiments. Previous work has focussed on exhaustive search algorithms, which in many practical, large-scale experiments is infeasible. We propose using simulated annealing to search for(More)
The normalisation constant in the distribution of a discrete random variable may not be available in closed form; in such cases the calculation of the likelihood can be computationally expensive. Approximations of the likelihood or approximate Bayesian computation (ABC) methods can be used; but the resulting MCMC algorithm may not sample from the target of(More)
The posterior distribution of the number of components k in a finite mixture satisfies a set of inequality constraints. The result holds irrespective of the parametric form of the mixture components and under assumptions on the prior distribution weaker than those routinely made in the literature on Bayesian analysis of finite mixtures. The inequality(More)
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