By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shuisheng Ye ; Coll. of Comput., Nanchang Hongkong Univ., Nanchang ; Hong Cai ; Rongguan Sun

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.

Published in:

2008 Fourth International Conference on Natural Computation  (Volume:3 )

Date of Conference:

18-20 Oct. 2008