Detection of Abrupt Changes in Statistical Models

Abstract

This dissertation investigates different types of disorder problems by using sequential procedures for on-line implementation. The problem is considered within the framework of detecting abrupt changes in an observed random process when the disorder can occur at unknown times. The focus of this work is on quickest detection methods for cumsum procedures implemented for different parametric and nonparametric nonlinearities and their performance evaluation. Both the non-Bayesian (Maximum-Likelihood) and the Bayesian frameworks are presented but the focus is mainly on non-Bayesian methods for which detailed analysis is provided. The use of Brownian motion approximations is also included and provides an additional viewpoint of analyzing the performance for both the non-Bayesian and Bayesian methods.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1991
Accession Number
ADA240116

Entities

People

  • David Aviv

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Networks
  • Change Detection
  • Computational Science
  • Data Science
  • Detection
  • Detectors
  • Differential Equations
  • Information Processing
  • Information Science
  • Mathematical Filters
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Computer Vision.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference