用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "application". The purpose of the toolkit is to facilitate creating experimental Bayes nets that analyze sequences of events. The toolkit provides code to help with the following: (a) creating Bayes nets. There are three classes of nodes defined, and to construct a Bayes net, you can write code that calls the constructors of these classes, and then you can create links among them. (b) displaying Bayes nets. There is code to create new windows and to draw Bayes nets in them. This includes drawing the nodes, the arcs, the labels, and various properties of nodes. (c) propagating a-posteriori probabilities. When one node's probability changes, the posterior probabilities of nodes downstream from it may need to change, too, depending on firing thresholds, etc. There is code in the toolkit to support that. (d) simulating events ("playing" event sequences) and having the Bayes net respond to them. This functionality is split over several files. Here are the files and the functionality that they represent. BayesNetNode.py: class definition for the basic node in a Bayes net. BayesUpdating.py: computing the a-posteriori probability of a node given the probabilities of its parents. InputNode.py: class definition for "input nodes". InputNode is a subclass of BayesNetNode. Input nodes have special features that allow them to recognize evidence items (using regular-expression pattern matching of the string descriptions of events). OutputNode.py: class definition for "output nodes". OutputBode is a subclass of BayesNetNode. An output node can have a list of actions to be performed when the node's posterior probability exceeds a threshold ReadWriteSigmaFiles.py: Functionality for loading and saving Bayes nets

提出了根据配电网的实际拓扑结构和元件对系统的影响关系直接建立贝叶斯网络以实现配电系统可靠性分析的方法。该方法的特点是不仅能进行配电网的可靠性指标评估,而且还能方便地得到系统每个元件或几个元件对整个系统可靠性的影响,从而克服了配电系统传统可靠性评估方法的不足。通过实例,阐述了应用贝叶斯网络方法进行配电系统可靠性评估的有效性和优越性。

贝叶斯网络

人工智能-贝叶斯网络

em算法matlab代码主轴-HMM 复制所有结果，表格和纸图的代码 要求 MATLAB-结果，表格和图2 MATLAB R2016b或更高版本（实现的算法需要广播功能） 统计和机器学习工具箱 信号处理工具箱 并行计算工具箱（可选，但强烈推荐用于基于EM的学习） Python-图1 Python3 jupyter笔记本 matplotlib 绘制概率图形模型 数据 DREAMS睡眠纺锤数据集 指示 将“ Main_Code”文件夹添加到MATLAB路径 下载DREAMS睡眠纺锤数据集。 可靠的来源（请确保下载DatabaseSpindles.rar文件）。 解压缩.rar文件。 运行reformatDREAMS.m脚本以对EEG和专家标签进行格式化和降采样 现在即可运行“ DREAMS”文件夹中的算法/脚本。 这些是用于复制结果，表和图2的脚本 （可选）使用matplotlib和RARHSMM_GraphicalModel.ipynb设置python环境，以在jupyter笔记本上运行RARHSMM_GraphicalModel.ipynb PS1：所有主要代码都是MATLAB，pyt

R语言bnlearn

可以实现在界面创建节点实现贝叶斯网络，可以弄清过程，结果可视化良好，做科研比较好。

基于matlab的贝叶斯网络工具箱BNT是kevin p.murphy基于matlab语言开发的关于贝叶斯网络学习的开源软件包，提供了许多贝叶斯网络学习的底层基础函数库，支持多种类型的节点（概率分布）、精确推理和近似推理、参数学习及结构学习、静态模型和动态模型。

在FULLBNT工具箱的基础上用matlab实现贝叶斯网络建模 概率分析

FullBNT-1.0.7.zip 最新的贝叶斯网络代码包