Please use this identifier to cite or link to this item:
Kreutz, Martin
Reimetz, Anja M.
Sendhoff, Bernhard
Weihs, Claus
von Seelen, Werner
Year of Publication: 
Series/Report no.: 
Technical Report 1999,27
We propose a new information theoretically based optimization criterion for the estimation of mixture density models and compare it with other methods based on maximum likelihood and maximum a posterio estimation. For the optimization, we employ an evolutionary algorithm which estimates both structure and parameters of the model. Experimental results show that the chosen approach compares favourably with other methods for estimation problems with few sample data as well as for problems where the underlying density is non-stationary.
Document Type: 
Working Paper
Social Media Mentions:

Files in This Item:
7.5 MB
878.75 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.