Statistical projectivity

April 26, 2020 — January 11, 2022

distributed
networks
probability
statistics

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

Figure 1

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.”