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Baba B. Alhaji Yoshiko Hayashi Veronica Vinciotti Andrew Harrison Berthold Lausen 《Journal of applied statistics》2016,43(8):1369-1385
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. 相似文献
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Lausen Emilia Danuta Backhaus Antje Jensen Marina Bergen Lausen Emilia Danuta 《Urban Ecosystems》2022,25(5):1577-1588
Urban Ecosystems - The popularity and use of green infrastructure measures such as green roofs, green walls, and curb extensions is growing, especially in dense urban areas. At the same time, from... 相似文献
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