An Algorithm for Bayesian Networks Structure Learning Based on Simulated Annealing with MDL Restriction

Author

Ye, Shuisheng ; Cai, Hong ; Sun, Rongguan

Author_Institution

Coll. of Comput., Nanchang Hongkong Univ., Nanchang

Volume

3

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

72

Lastpage

76

Abstract

By means of deducing and analyzing the minimum description length (MDL) principle as the grading functions, this paper designs a maximum entropy grade-function with complexity restriction, and proposes an algorithm for structure learning in Bayesian networks based on simulated annealing. Then according to the analysis of the historical stock data with this algorithm, the topological structure of network is obtained and so is conditional probability table of every network node. At last, the trend and fluctuation interval of the stock price are forecasted by this Bayesian model.

Keywords

Bayes methods; maximum entropy methods; pricing; simulated annealing; stock markets; Bayesian networks structure learning; MDL restriction; complexity restriction; grading functions; historical stock data; maximum entropy grade-function; minimum description length; simulated annealing; stock price; Algorithm design and analysis; Analytical models; Bayesian methods; Computational modeling; Computer networks; Entropy; Fluctuations; Network topology; Predictive models; Simulated annealing; Bayesian Networks (BN); MDL; simulated annealing; stock forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2008. ICNC '08. Fourth International Conference on

Conference_Location

Jinan

Print_ISBN

978-0-7695-3304-9

Type

conf

DOI

10.1109/ICNC.2008.658

Filename

4667104