• DocumentCode
    3754046
  • 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
  • fYear
    2015
  • Firstpage
    328
  • Lastpage
    332
  • 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"
  • Publisher
    ieee
  • Conference_Titel
    Signal and Information Processing (GlobalSIP), 2015 IEEE Global Conference on
  • Type

    conf

  • DOI
    10.1109/GlobalSIP.2015.7418211
  • Filename
    7418211