Title :
Optimum joint detection and estimation
Author :
Moustakides, George V.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Patras, Rion-Patras, Greece
fDate :
July 31 2011-Aug. 5 2011
Abstract :
We consider the problem of simultaneous binary hypothesis testing and parameter estimation. By defining suitable joint formulations we develop combined detection and estimation strategies that are optimum. Key point of the proposed methodologies constitutes the fact that they integrate both well known approaches, namely Bayesian and Neyman-Pearson.
Keywords :
belief networks; image segmentation; object detection; parameter estimation; Bayesian approach; Neyman-Pearson approach; binary hypothesis testing; optimum combined detection-estimation strategies; parameter estimation;
Conference_Titel :
Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on
Conference_Location :
St. Petersburg
Print_ISBN :
978-1-4577-0596-0
Electronic_ISBN :
2157-8095
DOI :
10.1109/ISIT.2011.6034125
