Bayesian Scalable Precision Factor Analysis for Gaussian Graphical Models-Reference-Cited by-同舟云学术

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Bayesian Scalable Precision Factor Analysis for Gaussian Graphical Models

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

Author:

Chandra Noirrit Kiran¹,Müller Peter²,Sarkar Abhra³

Affiliation:

1. Department of Mathematical Sciences, The University of Texas at Dallas, 800 W. Campbell Rd, Richardson, TX 75080-3021, USA

2. Department of Mathematics, The University of Texas at Austin, 2515 Speedway, PMA 8.100, Austin, TX 78712, USA

3. Department of Statistics and Data Sciences, The University of Texas at Austin, 105 East 24th Street, D9800 Austin, Texas 78712, USA

Publisher

Institute of Mathematical Statistics

Reference95 articles.

1. Ghosal, S. and van der Vaart, A. (2017). Fundamentals of Nonparametric Bayesian Inference. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press.

2. Jones, B., Carvalho, C., Dobra, A., Hans, C., Carter, C., and West, M. (2005). “Experiments in stochastic computation for high-dimensional graphical models.” Statistical Science, 20: 388–400.

3. Neal, R. M. (2000). “Markov Chain Sampling Methods for Dirichlet Process Mixture Models.” Journal of Computational and Graphical Statistics, 9: 249–265.

4. Atay-Kayis, A. and Massam, H. (2005). “A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models.” Biometrika, 92: 317–335.

5. Dawid, A. P. and Lauritzen, S. L. (1993). “Hyper Markov laws in the statistical analysis of decomposable graphical models.” The Annals of Statistics, 21: 1272–1317.

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