Abstract
The ubiquitous use of Dirichlet process models should not discourage researchers from considering interesting features of alternative models. In particular, the Polya tree model turns out to be an attractive choice for some applications. In this chapter we discuss the use of the Polya tree prior and its variations for density estimation. We define the model, introduce computation efficient methods for posterior inference and identify relative advantages and limitations compared with Dirichlet process models.
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Müller, P., Quintana, F.A., Jara, A., Hanson, T. (2015). Density Estimation: Models Beyond the DP. In: Bayesian Nonparametric Data Analysis. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18968-0_3
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DOI: https://doi.org/10.1007/978-3-319-18968-0_3
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