这篇论文是分布估计算法中的一篇基础文献，呵呵。个人认为还是很不错的。

课程作业，分布估计算法。 需要的话敬请下载。。。。。。

矩形件排样是一个平面二维优化布局的问题，由于其众多的约束条件和计算上的复杂性，在短时间内求其最优解相当困难，属于典型的NP完全问题。针对矩形件排样问题，本文采取一种改进的最低水平线搜索算法，通过判断排样中产生的废弃空闲区域的位置关系，对邻接的空闲区域进行有效的合并，并结合分布估计算法求解矩形件排样优化问题。最后，通过模拟实验，采用本文算法求解后矩形板材的利用率为93.75%，充分体现了本文算法的有效性。

Mateda2.0一个非常好用的分布估计算法matlab工具箱，For a preliminary explanation of Mateda2.0 see the file Mateda2.0-UserGuide.pdf in this directory. General documentation about the programs is available in the /doc directory or from: http://www.sc.ehu.es/ccwbayes/members/rsantana/software/matlab/MATEDA.html MATEDA-2.0 employs the Matlab Bayes Net (BNT) toolbox (Murphy:2001) and the BNT structure learning package (Leray_and_Francois:2004). These programs, which are freely available from the authors website (they can be respectively downloaded from http://people.cs.ubc.ca/~murphyk/Software/BNT/bnt.html and http://banquiseasi.insa-rouen.fr/projects/bnt-slp/), should be installed previously to the MATEDA-2.0 installation. Some of the MATEDA-2.0 routines also employs the MATLAB statistical toolbox and the affinity propagation clustering algorithm (Frey_and_Dueck:2007) (the Matlab implementation of affinity propagation is available from http://www.psi.toronto.edu/affinitypropagation/). After installing the BNT and BNT structure learning tools: 1) Set the path to the current BNT structure learning tool directory. This is done by modifying file add_SLP.m. 2) Unpack the file IntEDA.zip and copy the files to a directory named MATEDA. 3) Edit file InitEnvironment.m updating the paths path_MATEDA, path_FullBNT and path_BNT_SLP. 4) Set the current Matlab directory to the MATEDA directory. 5) Execute program InitEnvironments.m. Several warnings but no error should appear. The folder ScriptsMateda contains several examples of EDAs implementations. The file Mateda2.0-UserGuide.pdf contains a detailed explanation of how to use the programs. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Last version 2/04/2009. Roberto Santana (roberto.santana@ehu.es)

计算智能,pyhton:粒子群算法和分布估计算法，代码有注释

matlab程序,PBIL 分布估计 EDA算法

讲解了分布估计算法的基本原理，附带一个基于matlab实现的例子代码

该资源详细地给出了分布估计算法的PBIL算法的具体流程。供学习分布估计算的人参考使用。