创建你的第一个贝叶斯网络 手工创建一个模型 从一个文件加载一个模型 使用 GUI 创建一个模型 推断 处理边缘分布 处理联合分布 虚拟证据 最或然率解释 条件概率分布 列表（多项式）节点 Noisy-or 节点 其它（噪音）确定性节点 Softmax（多项式 分对数）节点 神经网络节点 根节点 高斯节点 广义线性模型节点 分类 / 回归树节点 其它连续分布 CPD 类型摘要 模型举例 高斯混合模型 PCA、ICA等 专家系统的混合 专家系统的分等级混合 QMR 条件高斯模型 其它混合模型 参数学习 从一个文件里加载数据 从完整的数据中进行最大似然参数估计 先验参数 从完整的数据中（连续）更新贝

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)

论文，基于Matlab工具箱的电力系统小干扰稳定性分析，可以了把

Kalman滤波Matlab工具箱，包含有关Kalman滤波的一些函数，包含一个简单的目标跟踪仿真程序

最新版，最小二乘支持向量机（2010年9月更新）matlab工具箱（内附说明文档（英文版），文档中附有例子）

matlab里可用的nurbs工具箱，可以用来进行生成nurbs曲线曲面。

LSD直线检测工具箱，可以通过配置，直接调用，详细的配置信息参见reco_toolbox目录下的read me.txt

1、集成了八种种群体智能算法：'PPNGA','SFLA','MSFLA','AF-SFLA','PSO','ABC','DEr1','DEb2' % 2、适应度函数开放了一个参数Parmaters，接收本文件Options.Parmaters传递的参数，实例参见m文件F3_Rastrigin.m % 3、本程序设置了三个终止条件，终止条件3是必需的；终止条件1,2是可选的，不需要时隐去代码即可 % 4、在优化参数设置中，小种群设置与大种群设置各有优势，实际工程问题不一定哪一种设置更好，要根据实验结果来确定 % 5、在算法参数设置中，缺省设置一般不需要改动，除非在原理上对改动所引起的结果变动有着更深入的理解

粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来的一种新的进化算法。PSO 算法属于进化算法的一种，和遗传算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单。压缩文件内附有英文说明书

http://www.mathworks.com/matlabcentral/fileexchange/25986-constrained-particle-swarm-optimization Description Previously titled "Another Particle Swarm Toolbox" Introduction Particle swarm optimization (PSO) is a derivative-free global optimum solver. It is inspired by the surprisingly organized behaviour of large groups of simple animals, such as flocks of birds, schools of fish, or swarms of locusts. The individual creatures, or "particles", in this algorithm are primitive, knowing only four simple things: 1 & 2) their own current location in the search space and fitness value, 3) their previous personal best location, and 4) the overall best location found by all the particles in the "swarm". There are no gradients or Hessians to calculate. Each particle continually adjusts its speed and trajectory in the search space based on this information, moving closer towards the global optimum with each iteration. As seen in nature, this computational swarm displays a remarkable level of coherence and coordination despite the simplicity of its individual particles. Ease of Use If you are already using the Genetic Algorithm (GA) included with MATLAB's Global Optimization Toolbox, then this PSO toolbox will save you a great deal of time. It can be called from the MATLAB command line using the same syntax as the GA, with some additional options specific to PSO. This will allow a high degree of code re-usability between the PSO toolbox and the GA toolbox. Certain GA-specific parameters such as cross-over and mutation functions will obviously not be applicable to the PSO algorithm. However, many of the commonly used options for the Genetic Algorithm Toolbox may be used interchangeably with PSO since they are both iterative population-based solvers. See >> help pso (from the ./psopt directory) for more details. Features * NEW: support for distributed computing using MATLAB's parallel computing toolbox. * Full support for bounded, linear, and nonlinear constraints. *