The scalable birth–death MCMC algorithm for mixed graphical model learning with application to genomic data integration-Reference-Cited by-同舟云学术

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The scalable birth–death MCMC algorithm for mixed graphical model learning with application to genomic data integration

Published:2023-09-01 Issue:3 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Wang Nanwei¹,Massam Hélène²,Gao Xin²,Briollais Laurent³

Affiliation:

1. Department of Mathematics and Statistics, University of New Brunswick

2. Department of Mathematics and Statistics, York University

3. Lunenfeld-Tanenbaum Research Institute of Sinai Health System and Dalla Lana School of Public Health (Biostatistics), University of Toronto

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference39 articles.

1. Stephens, M. (2000). Bayesian analysis of mixture models with an unknown number of components—an alternative to reversible jump methods. Ann. Statist. 28 40–74.

2. DOBRA, A. and MOHAMMADI, R. (2018). Loglinear model selection and human mobility. Ann. Appl. Stat. 12 815–845.

3. WASSERMAN, L. (2000). Bayesian model selection and model averaging. J. Math. Psych. 44 92–107.

4. BALDI, P., BRUNAK, S., CHAUVIN, Y., ANDERSEN, C. A. and NIELSEN, H. (2000). Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics 16 412–424.

5. BERNSTEIN, B., STAMATOYANNOPOULOS, J., COSTELLO, J. et al. (2010). The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28 1045–1048.

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