Various New Statistical Models for Modeling and Change Detection in Multidimensional Dynamic Networks

Abstract

In this project, we developed three new statistical models for characterizing the dynamic evolution of networks and detecting changes deviating from the normal evolving trajectory. Modeling the dynamics of evolving networks and detecting abnormal changes is of great interest in many domains. However, the fundamental methodological development in statistical modeling has been lacking. Through this project, the PI has not only significantly extended her research area and generated ample results, but also established industrial collaboration and introduced new educational materials. The PI was successfully promoted to Associate Professor with tenure, for which this grant served as one honorable recognition.

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Document Details

Document Type
Technical Report
Publication Date
Jan 04, 2014
Accession Number
ADA606729

Entities

People

  • Li Jing

Organizations

  • Arizona State University

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Algorithms
  • Change Detection
  • Communication Networks
  • Computational Science
  • Detection
  • Engineering
  • Gaussian Distributions
  • Gaussian Processes
  • Industrial Engineering
  • Information Science
  • Probability
  • Random Variables
  • Social Media
  • Social Networking Services
  • Students
  • Systems Engineering
  • Wireless Communications

Readers

  • Military History of the United States in the 20th Century.
  • Neural Network Machine Learning.
  • Research Science/Academic Research