Hilbertian sine as an absolute measure of Bayesian inference in ISR, homeland security, medicine, and defense
| Autor: | Tomasz Jannson, Thomas Forrester, Wenjian Wang, Juan Hodelin, Volodymyr Romanov, Andrew A. Kostrzewski |
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| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Computer science Bayesian probability False positives and false negatives Homeland security 02 engineering and technology computer.software_genre Bayesian inference Measure (mathematics) Identification (information) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Sine computer |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2220832 |
| Popis: | In this paper, Bayesian Binary Sensing (BBS) is discussed as an effective tool for Bayesian Inference (BI) evaluation in interdisciplinary areas such as ISR (and, C3I), Homeland Security, QC, medicine, defense, and many others. In particular, Hilbertian Sine (HS) as an absolute measure of BI, is introduced, while avoiding relativity of decision threshold identification, as in the case of traditional measures of BI, related to false positives and false negatives. |
| Databáze: | OpenAIRE |
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