Bayesian multiple instance classification based on hierarchical probit regression-Reference-Cited by-同舟云学术

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Bayesian multiple instance classification based on hierarchical probit regression

Published:2024-03-01 Issue:1 Volume:18 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Xiong Danyi¹,Park Seongoh²,Lim Johan³,Wang Tao⁴,Wang Xinlei⁵

Affiliation:

1. Department of Statistical Science, Southern Methodist University

2. Department of Statistics, Sungshin Women’s University

3. Department of Statistics, Seoul National University

4. Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center

5. Department of Mathematics, University of Texas at Arlington

Publisher

Institute of Mathematical Statistics

Reference51 articles.

1. Albert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. 88 669–679.

2. Polson, N. G., Scott, J. G. and Windle, J. (2013). Bayesian inference for logistic models using Pólya–Gamma latent variables. J. Amer. Statist. Assoc. 108 1339–1349.

3. CARBONNEAU, M.-A., CHEPLYGINA, V., GRANGER, E. and GAGNON, G. (2018). Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognit. 77 329–353.

4. Gelman, A., Jakulin, A., Pittau, M. G. and Su, Y.-S. (2008). A weakly informative default prior distribution for logistic and other regression models. Ann. Appl. Stat. 2 1360–1383.

5. Newton, M. A., Noueiry, A., Sarkar, D. and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 155–176.

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