A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures-Reference-Cited by-同舟云学术

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A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures

Published:2024-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

Horiguchi Akira¹,Chan Cliburn²,Ma Li³

Affiliation:

1. Department of Statistical Science, Duke University, Durham, NC 27708

2. Department of Biostatistics and Bioinformatics and Center for Human Systems Immunology, Duke University School of Medicine, Durham, NC 27708

3. Department of Statistical Science and Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708

Publisher

Institute of Mathematical Statistics

Reference83 articles.

1. Miller, J. W. and Harrison, M. T. (2018). “Mixture Models with a Prior on the Number of Components.” Journal of the American Statistical Association, 113(521): 340–356.

2. Favaro, S., Lijoi, A., and Prünster, I. (2012). “On the stick-breaking representation of normalized inverse Gaussian priors.” Biometrika, 99(3): 663–674.

3. Regazzini, E., Lijoi, A., and Prünster, I. (2003). “Distributional results for means of normalized random measures with independent increments.” The Annals of Statistics, 31(2): 560–585.

4. Jordan, M. I. and Jacobs, R. A. (1994). “Hierarchical Mixtures of Experts and the EM Algorithm.” Neural computation, 6(2): 181–214.

5. Peng, F., Jacobs, R. A., and Tanner, M. A. (1996). “Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models with an Application to Speech Recognition.” Journal of the American Statistical Association, 91(435): 953–960.

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