Research Seminar Series in Statistics and Mathematics
The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.
No registration required.
The list of talks for the summer term 2019 is available via the following link: https://www.wu.ac.at/en/statmath/resseminar
Modelling via finite mixtures is one of the most fruitful Bayesian approach, particularly useful for clustering when there is unobserved heterogeneity in the data. The most popular algorithm under this approach is the reversible jump MCMC that can be nontrivial to design, especially in high-dimensional spaces. We will show how nonparametric methods can be transferred into the parametric framework. We first introduce a class of almost sure finite discrete random probability measures obtained by normalization of finite point processes. Then, we use the new class as mixing measure of a mixture model and derive its posterior characterization. The resulting class encompasses the popular finite Dirichlet mixture model. In order to compute posterior statistics, we propose an alternative to the reversible jump: borrowing notation from the nonparametric Bayesian literature, we set up a conditional MCMC algorithm based on the posterior characterization of the unnormalized point process. The the flexibility of the model and the performances of our algorithm are illustrated on simulated and real data.
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