Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models
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DOI: 10.1016/j.csda.2016.10.015
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Cited by:
- George Karabatsos, 2023. "Approximate Bayesian computation using asymptotically normal point estimates," Computational Statistics, Springer, vol. 38(2), pages 531-568, June.
- Alex Stivala & Garry Robins & Alessandro Lomi, 2020. "Exponential random graph model parameter estimation for very large directed networks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-21, January.
- Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
- Manajit Chakraborty & Maksym Byshkin & Fabio Crestani, 2020. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-28, December.
- Pavel N. Krivitsky & Laura M. Koehly & Christopher Steven Marcum, 2020. "Exponential-Family Random Graph Models for Multi-Layer Networks," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 630-659, September.
- Kei, Yik Lun & Chen, Yanzhen & Madrid Padilla, Oscar Hernan, 2023. "A partially separable model for dynamic valued networks," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
- Babkin, Sergii & Stewart, Jonathan R. & Long, Xiaochen & Schweinberger, Michael, 2020. "Large-scale estimation of random graph models with local dependence," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
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Keywords
Curved exponential family; ERGM; Network data; Partial stepping;All these keywords.
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