We briefly review some of the nonparametric Bayesians models that are most widely used in biostatistics and bioinformatics. We define the Dirichlet process, Dirichlet process mixtures, the Polya tree, the dependent Dirichlet process and the Gaussian process prior. These few models and variations cover a major part of the models that are used in the literature. The discussion includes references to variations of the basic models that are defined in the chapters of this volume.
CITATION STYLE
Müller, P., & Mitra, R. (2015). Bayesian nonparametric models. In Nonparametric Bayesian Inference in Biostatistics (pp. 3–14). Springer International Publishing. https://doi.org/10.1007/978-3-319-19518-6_1
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