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http://bura.brunel.ac.uk/handle/2438/11603
Title: | Bayesian analysis for mixtures of discrete distributions with a non-parametric component |
Authors: | Alhaji, BB Dai, H Hayashi, Y Vinciotti, V Harrison, A Lausen, B |
Keywords: | Bayesian;Label switching;Mixture model;Gibbs sampler |
Issue Date: | 2015 |
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. |
URI: | http://www.tandfonline.com/doi/full/10.1080/02664763.2015.1100594 http://bura.brunel.ac.uk/handle/2438/11603 |
DOI: | http://dx.doi.org/10.1080/02664763.2015.1100594 |
ISSN: | 1360-0532 |
Appears in Collections: | Dept of Mathematics Research Papers |
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