Posterior contraction rate of sparse latent feature models with application to proteomics

Posterior contraction rate of sparse latent feature models with application to proteomics

The Indian buffet process (IBP) and phylogenetic Indian buffet process (pIBP) can be used as prior models to infer latent features in a data set. The theoretical properties of these models are under-explored, however, especially in high dimensional settings. In this paper, we show that under mild sp...

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Bibliographic Details
Main Authors: Tong Li, Tianjian Zhou, Kam-Wah Tsui, Lin Wei, Yuan Ji
Format: Article
Language:English
Published: Taylor & Francis Group 2022-01-01
Series:Statistical Theory and Related Fields
Subjects:
high dimension
indian buffet process
latent feature
markov chain monte carlo
posterior convergence
reverse phase protein arrays
Online Access:http://dx.doi.org/10.1080/24754269.2021.1974664

Internet

http://dx.doi.org/10.1080/24754269.2021.1974664

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