Bayesian Characterization and Detection of Rare Binary Signature Events
Abstract
A method of imposing a binary classification on a target signature is described that transforms a sequence of signatures into a binary-valued time series using the theory of runs. Under the assumption that the time series can be treated as a set of Bernoulli trials, a Bayesian method of estimating a probability density characterizing the outcome of each trial is considered. The density is used to detect signature events which are found to be rare with respect to the classification imposed on the signatures. Finally, tests of homogeneity are used to partition the observed signatures, when necessary, into equivalence classes having the same density characterization. (kr)
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 15, 1990
- Accession Number
- ADA221116
Entities
People
- Richard C. Raup
Organizations
- Massachusetts Institute of Technology