Bayesian Change Detection
| dc.contributor.advisor | Baras, J.S. | en_US |
| dc.contributor.author | MacEnany, David C. | en_US |
| dc.contributor.department | ISR | en_US |
| dc.date.accessioned | 2007-05-23T09:49:22Z | |
| dc.date.available | 2007-05-23T09:49:22Z | |
| dc.date.issued | 1991 | en_US |
| dc.description.abstract | In this thesis we consider certain problems of optimal change detection in which the task is to decide in a sequential manner which of two probabilistic system descriptions account for given, observed data. Optimal decisions are defined according to an average cost criterion which has a penalty which increases with time and a penalty for incorrect decisions. We consider observation processes of both the diffusion and point process kind. A main result is a verification-type theorem which permits one to prove the optimality of candidate decision policies provided one can find a certain function and interval. The form of the theorem suggests how to go about looking for such a pair. As applications we consider the sequential detection and disruption problems involving diffusion observations and give new proofs of the existence of the optimal thresholds as well as a new, simple algorithm for their computation. In the case of sequential detection between Poisson processes we solve the so- called overshoot problem exactly for the first time using the same algorithm. | en_US |
| dc.format.extent | 11709984 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1903/5166 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | ISR; PhD 1991-8 | en_US |
| dc.subject | detection | en_US |
| dc.subject | filtering | en_US |
| dc.subject | signal processing | en_US |
| dc.subject | Systems Integration | en_US |
| dc.title | Bayesian Change Detection | en_US |
| dc.type | Dissertation | en_US |
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