Title :
New insights on stochastic complexity
Author :
Giurcaneanu, Ciprian Doru ; Razavi, Seyed Alireza
Author_Institution :
Dept. of Signal Process., Tampere Univ. of Technol., Tampere, Finland
Abstract :
The Minimum Description Length (MDL) principle led to various expressions of the stochastic complexity (SC), and the most recent one is given by the negative logarithm of the Normalized Maximum Likelihood (NML). For better understanding the properties of the newest SC-formula, we relate it to the well-known Generalized Likelihood Ratio Test (GLRT). Additionally, we compare the SC with the Bayesian Information Criterion (BIC) and other model selection rules. Some of the results are discussed in connection with families of models that are widely used in signal processing.
Keywords :
maximum likelihood estimation; signal processing; stochastic processes; BIC; Bayesian information criterion; GLRT; MDL principle; NML; generalized likelihood ratio test; minimum description length principle; model selection rules; negative logarithm; normalized maximum likelihood; signal processing; stochastic complexity; Complexity theory; Mathematical model; Maximum likelihood estimation; Signal processing; Silicon; Stochastic processes; Testing;
Conference_Titel :
Signal Processing Conference, 2009 17th European
Conference_Location :
Glasgow
Print_ISBN :
978-161-7388-76-7
