Detection of abrupt changes in statistical models
Therrien, Charles W.
MetadataShow full item record
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.
Approved for public release; distribution is unlimited
Showing items related by title, author, creator and subject.
A comparison of classical and Bayesian methods for determining lower confidence limits on system reliability. Kirk, Gary Lee (1972-09);A series system is simulated to obtain lower confidence limits on system reliability using Bayesian techniques. A comparison between classical and Bayesian methods is made. Random beta variate generators are developed ...
Kalkwarf, Benjamin (Monterey, California: Naval Postgraduate School, 2017-09);Search and Detection Theory is the overarching field of study that covers many scenarios. These range from simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at ...
Bernales, Barton J. (Monterey, California. Naval Postgraduate School, 1995-09);This thesis investigates the applicability and results of a Bayesian approach used to forecast the future direction of the Cuban economy. The Castro regime, bound to a stagnant political ideology, has limited the options ...