Signal analysis by stochastic complexity

Talk announcement:
HIIT Seminar Kumpula, Friday June 10 10:15, Exactum C222

Speaker:
Ciprian Doru Giurcaneanu
Tampere University of Technology

Title:
Signal analysis by stochastic complexity

Abstract:
The talk will be focused on the applications of the stochastic
complexity (SC), which has been introduced by Prof. Jorma Rissanen in
the framework of model selection. During recent years, we have proposed
various solutions based on SC for solving the following problems: (1)
Variable selection in Gaussian linear regression; (2) AR order selection
when  the coefficients of the model are estimated with forgetting-factor
least-squares algorithms; (3) Estimation of the number of sine-waves in
Gaussian noise when the sample size is small; (4) Quantifying the
dependence between time series, with applications to the EEG analysis in
a mild epileptic paradigm; (5) Composite hypothesis testing by optimally
distinguishable distributions. During the talk, we will provide a short
overview of the results outlined above by emphasizing the superiority of
SC in comparison with other methods.

Biography:
Ciprian Doru Giurcaneanu received his Ph.D. degree (with honors) from
the Department of Information Technology, Tampere University of
Technology, Finland, in 2001. From 1993 to 1997, he was a Junior
Assistant at ``Politehnica'' University of Bucharest, and since 1997 he
has been with Tampere University of Technology holding various research
positions. He is currently a Research Fellow of the Academy of Finland.
His research is focused on stochastic complexity and its applications.


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