# Statistical projectivity

April 26, 2020 — January 11, 2022

distributed

networks

probability

statistics

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

## 1 References

Balog, and Teh. 2015. “The Mondrian Process for Machine Learning.”

*arXiv:1507.05181 [Cs, Stat]*.
Cai, Campbell, and Broderick. 2016. “Edge-Exchangeable Graphs and Sparsity.” In

*Proceedings of the 30th International Conference on Neural Information Processing Systems*. NIPS’16.
Jaeger, and Schulte. 2021. “A Complete Characterization of Projectivity for Statistical Relational Models.” In

*Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence*. IJCAI’20.
Lakshminarayanan, Roy, and Teh. 2014. “Mondrian Forests: Efficient Online Random Forests.” In

*Advances in Neural Information Processing Systems 27*.
Orbanz. 2011. “Conjugate Projective Limits.”

Shalizi, and Rinaldo. 2013. “Consistency Under Sampling of Exponential Random Graph Models.”

*Annals of Statistics*.
Snijders. 2010. “Conditional Marginalization for Exponential Random Graph Models.”

*The Journal of Mathematical Sociology*.
Spencer, and Shalizi. 2020. “Projective, Sparse, and Learnable Latent Position Network Models.”

*arXiv:1709.09702 [Math, Stat]*.
Veitch, and Roy. 2015. “The Class of Random Graphs Arising from Exchangeable Random Measures.”

*arXiv:1512.03099 [Cs, Math, Stat]*.
Weitkämper. 2023. “Projectivity Revisited.”

*International Journal of Approximate Reasoning*.
Ye, Yang, Siah, et al. 2024. “Pre-Training and in-Context Learning IS Bayesian Inference a La De Finetti.”