DocumentCode :
1985167
Title :
A fast method for prior probability selection based on maximum entropy principle and Gibbs sampler
Author :
Dianat, R. ; Kasaei, S. ; Khabbazian, M.
Author_Institution :
Sharif Univ. of Technol., Tehran
fYear :
2007
fDate :
12-15 Feb. 2007
Firstpage :
1
Lastpage :
4
Abstract :
One of the problems in Bayesian inference is the prior selection. We can categorize different methods for selecting prior into two main groups: informative and non-informative. Here, we have considered an informative method called filters random filed and minimax entropy (FRAME). Despite of its theoretical interest, that method introduces a huge amount of computational burden, which makes it very unsuitable for real-time applications. The main critical point of the method is its parameter estimation part, which plays a major role in its very low speed. In this paper, we have introduced a fast method for parameter estimation to fasten the FRAME approach. Although the kernel of our approach is the Gibbs sampler that intrinsically has very low speed, our proposed method has led to a proper speed.
Keywords :
Bayes methods; filtering theory; maximum entropy methods; minimax techniques; parameter estimation; probability; signal sampling; Bayesian inference; FRAME approach; Gibbs sampler; filter random filed-minimax entropy method; maximum entropy; parameter estimation; prior probability selection; Bayesian methods; Costs; Density functional theory; Entropy; Filters; Kernel; Minimax techniques; Parameter estimation; Signal sampling; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on
Conference_Location :
Sharjah
Print_ISBN :
978-1-4244-0778-1
Electronic_ISBN :
978-1-4244-1779-8
Type :
conf
DOI :
10.1109/ISSPA.2007.4555332
Filename :
4555332
Link To Document :
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