Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
PhFit: A General Phase-Type Fitting Tool
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Acyclic discrete phase type distributions: properties and a parameter estimation algorithm
Performance Evaluation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Approximation of Discrete Phase-Type Distributions
ANSS '05 Proceedings of the 38th annual Symposium on Simulation
Bayesian Sequential Change Diagnosis
Mathematics of Operations Research
Multihypothesis sequential probability ratio tests .I. Asymptotic optimality
IEEE Transactions on Information Theory
Sequential multiple hypothesis testing and efficient fault detection-isolation in stochastic systems
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A simple recursive algorithm for diagnosis of abrupt changes in random signals
IEEE Transactions on Information Theory
Online activity detection in a multiuser environment using the matrix CUSUM algorithm
IEEE Transactions on Information Theory
A lower bound for the detection/isolation delay in a class of sequential tests
IEEE Transactions on Information Theory
A generalized change detection problem
IEEE Transactions on Information Theory
| Hi-index | 754.85 |
The problem of detection and identification of an unobservable change in the distribution of a random sequence is studied via a hidden Markov model (HMM) approach. The formulation is Bayesian, on-line, discrete-time, allowing both single- and multiple- disorder cases, dealing with both independent and identically distributed (i.i.d.) and dependent observations scenarios, allowing for statistical dependencies between the change-time and change-type in both the observation sequence and the risk structure, and allowing for general discrete-time disorder distributions. Several of these factors provide useful new generalizations of the sequential analysis theory for change detection and/or hypothesis testing, taken individually. In this paper, a unifying framework is provided that handles each of these considerations not only individually, but also concurrently. Optimality results and optimal decision characterizations are given as well as detailed examples that illustrate the myriad of sequential change detection and identification problems that fall within this new framework.