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• W2965718378 abstract "Variable selection and classification are common objectives in the analysis of high-dimensional data. Most such methods make distributional assumptions that may not be compatible with the diverse families of distributions data can take. A novel Bayesian nonparametric discriminant analysis model that performs both variable selection and classification within a seamless framework is proposed. Pólya tree priors are assigned to the unknown group-conditional distributions to account for their uncertainty, and allow prior beliefs about the distributions to be incorporated simply as hyperparameters. The adoption of collapsed variational Bayes inference in combination with a chain of functional approximations led to an algorithm with low computational cost. The resultant decision rules carry heuristic interpretations and are related to an existing two-sample Bayesian nonparametric hypothesis test. By an application to some simulated and publicly available real datasets, the proposed method exhibits good performance when compared to current state-of-the-art approaches." @default.
• W2965718378 created "2019-08-13" @default.
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• W2965718378 date "2020-02-01" @default.
• W2965718378 modified "2023-09-23" @default.
• W2965718378 title "Variational nonparametric discriminant analysis" @default.
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• W2965718378 doi "https://doi.org/10.1016/j.csda.2019.106817" @default.
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