Abstract
In large discrete data sets which requires classification into signal and noise components, the distribution of the signal is often very bumpy and does not follow a standard distribution. Therefore the signal distribution is further modelled as a mixture of component distributions. However, when the signal component is modelled as a mixture of distributions, we are faced with the challenges of justifying the number of components and the label switching problem (caused by multi-modality of the likelihood function). To circumvent these challenges, we propose a non-parametric structure for the signal component. This new method is more efficient in terms of precise estimates and better classifications. We demonstrated the efficacy of the methodology using a ChIP-sequencing data set.
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Alhaji, B.B., Dai, H., Hayashi, Y., Vinciotti, V., Harrison, A., Lausen, B. (2016). Analysis of ChIP-seq Data Via Bayesian Finite Mixture Models with a Non-parametric Component. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_43
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DOI: https://doi.org/10.1007/978-3-319-25226-1_43
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