Title :
Atypicality for vector Gaussian models
Author :
Elyas Sabeti;Anders H?st-Madsen
Author_Institution :
Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, HI, 96822
Abstract :
Atypical sequences are subsequences of long sequences that deviate from the `normal´ data. In previous papers we have developed an information-theoretic approach to such sequences for discrete and real-valued data. In the current paper we extend the principle of real-valued data that follows vector Gaussian models, which allows for finding relationship between data. We include a simple application to stock market data.
Keywords :
"Data models","Encoding","Conferences","Information processing","Complexity theory","Maximum likelihood estimation","Big data"
Conference_Titel :
Signal and Information Processing (GlobalSIP), 2015 IEEE Global Conference on
DOI :
10.1109/GlobalSIP.2015.7418211