Computationally efficient inference for latent position network models-Reference-Cited by-同舟云学术

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Computationally efficient inference for latent position network models

Published:2024-01-01 Issue:1 Volume:18 Page:
ISSN:1935-7524
Container-title:Electronic Journal of Statistics
language:
Short-container-title:Electron. J. Statist.

Author:

Rastelli Riccardo¹,Maire Florian²,Friel Nial³

Affiliation:

1. School of Mathematics and Statistics, University College Dublin

2. Department of Mathematics and Statistics, Université de Montréal

3. School of Mathematics and Statistics, University College Dublin Insight Centre for Data Analytics, University College Dublin

Publisher

Institute of Mathematical Statistics

Reference39 articles.

1. HANDCOCK, M. S., RAFTERY, A. E. and TANTRUM, J. M. (2007). Model-based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society) 170 301–354.

2. HOFF, P. D., RAFTERY, A. E. and HANDCOCK, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association 97 1090–1098.

3. Roberts, G. O. and Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys 1 20–71.

4. GILBERT, E. N. (1961). Random Plane Networks. Journal of the Society for Industrial and Applied Mathematics 9 533–543.

5. Nowicki, K. and Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American statistical association 96 1077–1087.

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