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Maximum Entropy Methods for Graph-Valued Random Objects



D L. Banks


Many problems entail the analysis of data that are independent and identically distributed random objects, such as graphs, sequences, partitions, and permutations. Additionally, the analysis can be further complicated by the presence of equivalence clases among the objects induced by the operation of some group. Useful inference requires flexible probability models for such situations; these models should have interpretable location and scale parameters, and support the establishment of confidence regions, maximum likelihood estimates, goodness-of-fit tests, and an appropriate analouge of linear model theory. Banks and Carley (1994) and Banks and Constantine (1998) develop a simple probability model and sketch some analyses; this research extends that work so that analysts are able to choose models that reflect more application-specific metrics on the set of objects. The procedure is illustrated through the analysis of data that are cluster trees.
Computing Science and Statistics


group therapy, metrics, random graphs, trees


Banks, D. (1998), Maximum Entropy Methods for Graph-Valued Random Objects, Computing Science and Statistics (Accessed May 29, 2024)


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Created September 30, 1998, Updated February 17, 2017