Statistical projectivity



Placeholder for a thing which turns out to be important but which is only sometimes explicit. Cosma wrote a good explanation.

References

Balog, Matej, and Yee Whye Teh. 2015. The Mondrian Process for Machine Learning.” arXiv:1507.05181 [Cs, Stat], July.
Broderick, Tamara, and Diana Cai. 2016. Edge-Exchangeable Graphs and Sparsity.” arXiv:1603.06898 [Math, Stat], March.
Cai, Diana, Trevor Campbell, and Tamara Broderick. 2016. Edge-Exchangeable Graphs and Sparsity.” In Proceedings of the 30th International Conference on Neural Information Processing Systems, 4249–57. NIPS’16. USA: Curran Associates Inc.
Jaeger, Manfred, and Oliver Schulte. 2020. A Complete Characterization of Projectivity for Statistical Relational Models.” arXiv:2004.10984 [Cs, Stat], April.
Lakshminarayanan, Balaji, Daniel M Roy, and Yee Whye Teh. 2014. Mondrian Forests: Efficient Online Random Forests.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 3140–48. Curran Associates, Inc.
Shalizi, Cosma Rohilla, and Alessandro Rinaldo. 2013. Consistency Under Sampling of Exponential Random Graph Models.” Annals of Statistics 41 (2): 508–35.
Snijders, Tom A. B. 2010. Conditional Marginalization for Exponential Random Graph Models.” The Journal of Mathematical Sociology 34 (4): 239–52.
Spencer, Neil A., and Cosma Rohilla Shalizi. 2020. Projective, Sparse, and Learnable Latent Position Network Models.” arXiv:1709.09702 [Math, Stat], February.
Veitch, Victor, and Daniel M. Roy. 2015. The Class of Random Graphs Arising from Exchangeable Random Measures.” arXiv:1512.03099 [Cs, Math, Stat], December.

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