GeNIe几乎算是构建分析贝叶斯网络最简单的模型之一，不需要编程，方便易行。教程为2020版最新教程

关于贝叶斯网络的习题，网上很少的，都是理论，公式，如果没有习题很难让人有兴趣阅读下去。本资源里边精选了贝叶斯网络学习的习题20道，方便大家学习

贝叶斯网络在人脸识别中的应用,描述贝叶斯网络的构建，组成，以及在人脸识别中的应用。

可视化软件，UI非常友好 省去了Matlab编程的烦恼，极适合新接触BN的用户使用亲测好用，挺不错的资源，需要的人，就快来下载吧！很有用的！

这本书主要是阐述传统的概率图模型的，对于现在火热的深度学习来说，正好。

贝叶斯网络

在大数据时代，传感器网络，社交网络，互联网等不断且快速地生成大量数据。从大数据流中学习知识是一项重要任务，因为它可以支持在线决策。 预测是有用的学习任务之一，但是固定模型通常不能很好地工作，因为数据分布会随时间而变化。 本文提出了一种基于演化贝叶斯网络的流数据预测方法。 贝叶斯网络模型是基于高斯混合模型和EM算法来推导的。 为了支持基于流数据的演化模型结构和参数，提出了一种演化爬山算法，该算法基于到达新数据时分数度量的增量计算。 实验评估表明，该方法是有效的，并且优于流式数据预测的其他流行方法。

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