Connections between probability estimation and graph theory

Authors:
N. P. Santhanam
Affiliations:
Department of Electrical Engineering, University of Hawaii, Honolulu, HI
Venue:
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Year:
2009

Citing 6
Cited 0

Graph limits and parameter testing

Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
The rank of connection matrices and the dimension of graph algebras

European Journal of Combinatorics
A lower bound on compression of unknown alphabets

Theoretical Computer Science
Limits of dense graph sequences

Journal of Combinatorial Theory Series B
Universal compression of memoryless sources over unknown alphabets

IEEE Transactions on Information Theory
Speaking of infinity [i.i.d. strings]

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several authors [1], [2], [3], [4], [5], [6], [7] have characterized limits of graph sequences in terms of extremal distributions on exchangeable random graphs. Parallely, information theorists and statisticians have used patterns of i.i.d. sequences as a framework to develop probability estimation techniques over large alphabets. In this brief note, we observe that measures over patterns of infinite i.i.d. sequences correspond to extremal distributions on exchangeable random graphs.