package subjectivebayes; import java.awt.Toolkit; import javax.swing.SwingUtilities; import javax.swing.UIManager; import java.awt.Dimension; /** * Title: * * Description: * * Copyright: Copyright (c) 2010 * * Company: * * @author not attributable * @version 1.0 */ public class MyApp { boolean packFrame = false; /** * Construct and show the application. */ public MyApp() { EnterBayes frame = new EnterBayes(); // Validate frames that have preset sizes // Pack frames that have useful preferred size info, e.g. from their layout if (packFrame) { frame.pack(); } else { frame.validate(); } // Center the window Dimension screenSize = Toolkit.getDefaultToolkit().getScreenSize(); Dimension frameSize = frame.getSize(); if (frameSize.height > screenSize.height) { frameSize.height = screenSize.height; } if (frameSize.width > screenSize.width) { frameSize.width = screenSize.width; } frame.setLocation((screenSize.width - frameSize.width) / 2, (screenSize.height - frameSize.height) / 2); frame.setVisible(true); } /** * Application entry point. * * @param args String[] */ public static void main(String[] args) { SwingUtilities.invokeLater(new Runnable() { public void run() { try { UIManager.setLookAndFeel(UIManager. getSystemLookAndFeelClassName()); } catch (Exception exception) { exception.printStackTrace(); } new MyApp(); } }); } }

5.4主观Bayes方法 5.4.1 知识不确定性的表示 1. 知识表示方法 在主观Bayes方法中，知识是用产生式表示的，其形式为 IFETHEN(LS，LN) H 其中(LS，LN)用来表示该知识的知识强度，LS和LN的表示形式分别为 LS=P(E/H)/P(E/~H)  LN=P(~E/H) /P(~E/~H)=(1-P(E/H))/(1-P(E/~H)) LS和LN的取值范围均为［0，+∞］。 由Bayes公式可知 P(H/E)=P(E/H)×P(H)/P(E) P(~H/E)=P(E/~H)×P(~H)/P(E) 将两式相除，得 P(H/E)/P(~H/E)=P(E/H)/P(E/~H)×P(H)/P(~H) 