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Nonparametric Bayesian Statistical Models in Biomedical Research
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 Title & Authors
Nonparametric Bayesian Statistical Models in Biomedical Research
Noh, Heesang; Park, Jinsu; Sim, Gyuseok; Yu, Jae-Eun; Chung, Yeonseung;
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 Abstract
Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.
 Keywords
Nonparametric Bayes;Dirichlet process;density estimation;clustering;random effects distribution;regression;
 Language
Korean
 Cited by
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