Abstract
An important byproduct of inference in discrete mixture models is an implied random partition of experimental units. In fact, such random partitions are the main inference targets for many recently published applications of nonparametric Bayesian discrete mixture models. In this chapter we systematically consider the use of nonparametric Bayesian priors for inference on such random partitions. Many scientific inference problems are formalized as the related, more general problem of feature allocation. That is, inference on possibly overlapping random subsets of experimental units. We introduce some examples from data analysis for bioinformatics data and introduce the Polya urn model, product partition models, model based clustering and the Indian buffet process prior.
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Müller, P., Quintana, F.A., Jara, A., Hanson, T. (2015). Clustering and Feature Allocation. In: Bayesian Nonparametric Data Analysis. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18968-0_8
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DOI: https://doi.org/10.1007/978-3-319-18968-0_8
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