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for search: '"Stewart, Jonathan"'
Author/Creator:
Stewart, Jonathan R.
Local dependence random graph models are a class of block models for network data which allow for dependence among edges under a local dependence assumption defined around the block structure of the network. Since being introduced by Schweinberger an
Externí odkaz:
http://arxiv.org/abs/2404.11464
Author/Creator:
Stewart, Jonathan R.
One of the first steps in applications of statistical network analysis is frequently to produce summary charts of important features of the network. Many of these features take the form of sequences of graph statistics counting the number of realized
Externí odkaz:
http://arxiv.org/abs/2404.11438
Author/Creator:
Li, Jiaheng, Stewart, Jonathan R.
Multilayer networks are a network data structure in which elements in a population of interest have multiple modes of interaction or relation, represented by multiple networks called layers. We propose a novel class of models for cross-layer dependen
Externí odkaz:
http://arxiv.org/abs/2307.14982
Author/Creator:
Hidalgo, Jairo Ivan Peña, Stewart, Jonathan R.
We introduce a new methodology for model selection in the context of modeling network data. The statistical network analysis literature has developed many different classes of network data models, with notable model classes including stochastic block
Externí odkaz:
http://arxiv.org/abs/2301.02871
Author/Creator:
Garfinkel, Simson, Stewart, Jonathan
Bulk_extractor is a high-performance digital forensics tool written in C++. Between 2018 and 2022 we updated the program from C++98 to C++17, performed a complete code refactoring, and adopted a unit test framework. The new version typically runs wit
Externí odkaz:
http://arxiv.org/abs/2208.01639
Author/Creator:
Leggett, Nina, Emery, Kate, Rollinson, Thomas C., Deane, Adam M., French, Craig, Manski-Nankervis, Jo-Anne, Eastwood, Glenn, Miles, Briannah, Witherspoon, Sophie, Stewart, Jonathan, Merolli, Mark, Ali Abdelhamid, Yasmine, Haines, Kimberley J.
Published in:
In Chest July 2024 166(1):95-106
Author/Creator:
Ducharme, Lori J., Fujimoto, Kayo, Kuo, Jacky, Stewart, Jonathan, Taylor, Bruce, Schneider, John
Published in:
In Evaluation and Program Planning February 2024 102
Author/Creator:
Stewart, Jonathan R., Schweinberger, Michael
An important question in statistical network analysis is how to estimate models of discrete and dependent network data with intractable likelihood functions, without sacrificing computational scalability and statistical guarantees. We demonstrate tha
Externí odkaz:
http://arxiv.org/abs/2012.07167
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