DocumentCode
3146260
Title
Asymptotics of predictive stochastic complexity
Author
Gerencser, Laszlo
Author_Institution
Comput Vision & Robotics Lab., McGill Univ., Montreal, Que., Canada
fYear
1991
fDate
8-11 Apr 1991
Firstpage
228
Lastpage
238
Abstract
This paper presents the basic ideas of the theory of stochastic complexity with rigorous asymptotic results in the field of time-series analysis and system identification, which demonstrate the applicability of stochastic complexity to these difficult statistical problems
Keywords
computational complexity; filtering and prediction theory; identification; statistical analysis; stochastic systems; time series; asymptotic results; predictive stochastic complexity; statistical problems; system identification; time-series analysis; Computer vision; Density functional theory; Intelligent robots; Laboratories; Robot vision systems; Robotics and automation; Stochastic processes; Stochastic systems; Telephony; Time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Compression Conference, 1991. DCC '91.
Conference_Location
Snowbird, UT
Print_ISBN
0-8186-9202-2
Type
conf
DOI
10.1109/DCC.1991.213358
Filename
213358
Link To Document