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|Title:||Bayesian analysis for mixtures of discrete distributions with a non-parametric component|
|Keywords:||Bayesian;Label switching;Mixture model;Gibbs sampler|
|Publisher:||Taylor & Francis (Routledge)|
|Citation:||Journal of Applied Statistics, 2015|
|Abstract:||Bayesian finite mixture modelling is a flexible parametric modelling approach for classification and density fitting. Many areas of application require distinguishing a signal from a noise component. In practice, it is often difficult to justify a specific distribution for the signal component; therefore, the signal distribution is usually further modelled via a mixture of distributions. However, modelling the signal as a mixture of distributions is computationally non-trivial due to the difficulties in justifying the exact number of components to be used and due to the label switching problem. This paper proposes the use of a non-parametric distribution to model the signal component. We consider the case of discrete data and show how this new methodology leads to more accurate parameter estimation and smaller false non-discovery rate. Moreover, it does not incur the label switching problem. We show an application of the method to data generated by ChIP-sequencing experiments.|
|Appears in Collections:||Dept of Mathematics Research Papers|
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