Statistical Methods for Percolation in Practice: Random Graph Hidden Markov Models

Abstract

The goal of this program of research is to develop a class of random graph hidden Markov models(RG-HMMs) for characterizing percolation in noisy, dynamically evolving networks, as well as a set of procedures for statistical estimation and hypothesis testing with these models, in a computationally scaleable manner.

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Document Details

Document Type
Technical Report
Publication Date
Oct 27, 2021
Accession Number
AD1205920

Entities

People

  • Eric D. Kolaczyk

Organizations

  • Boston University

Tags

Communities of Interest

  • Biomedical
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Behavioral Sciences
  • Computational Complexity
  • Computational Science
  • Data Science
  • Deep Learning
  • Epilepsy
  • Hidden Markov Models
  • Information Science
  • Markov Models
  • Models
  • Network Science
  • Percolation
  • Public Health
  • Social Media
  • Statistical Estimation
  • Statistical Inference
  • Statistics
  • Students
  • Universities

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Parallel and Distributed Computing.