Bayesian Network

贝叶斯网络原理
最新推荐文章于 2024-11-01 22:26:48 发布
原创 最新推荐文章于 2024-11-01 22:26:48 发布 · 744 阅读 · 0 ·
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本文档详细介绍了贝叶斯网络的基本原理，包括其数学模型及应用场景。作者分享了一个包含大量公式的文档，可供读者深入了解这一概率图模型的核心概念。

忙里偷闲，整理了一个关于beyasian network 基本原理的概念的文档，由于公式过多，需要的朋友可以在http://www.941hk.com/link.php?ref=eerceKEQL2 下载。水平有限，如果有错误，请给我留言，我会在第一时间修改，如果不能下载页可以给我留言。 

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动态贝叶斯网络DBN
tanzhangwen的专栏
12-05 3万+
贝叶斯网络 贝叶斯网络（Bayesian Networks）也被称为信念网络（Belif Networks）或者因果网络（Causal Networks），是描述数据变量之间依赖关系的一种图形模式，是一种用来进行推理的模型。贝叶斯网络为人们提供了一种方便的框架结构来表示因果关系，这使得不确定性推理变得在逻辑上更为清晰、可理解性强。对于贝叶斯网络，我们可以用两种方法来看待它：首先贝叶斯网表达了各个节
Bayesian Network（贝叶斯网络） Python Program
04-26
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 in an XML format. SampleNets.py: Some code that constructs a sample Bayes net. This is called when SIGMAEditor.py is started up. SIGMAEditor.py: A main program that can be turned into an experimental application by adding menus, more code, etc. It has some facilities already for loading event sequence files and playing them. sample-event-file.txt: A sequence of events that exemplifies the format for these events. gma-mona.igm: A sample Bayes net in the form of an XML file. The SIGMAEditor program can read this type of file. Here are some limitations of the toolkit as of 23 February 2009: 1. Users cannot yet edit Bayes nets directly in the SIGMAEditor. Code has to be written to create new Bayes nets, at this time. 2. If you select the File menu's option to load a new Bayes net file, you get a fixed example: gma-mona.igm. This should be changed in the future to bring up a file dialog box so that the user can select the file. 3. When you "run" an event sequence in the SIGMAEditor, the program will present each event to each input node and find out if the input node's filter matches the evidence. If it does match, that fact is printed to standard output, but nothing else is done. What should then happen is that the node's probability is updated according to its response method, and if the new probability exceeds the node's threshold, then its successor ("children") get their probabilities updated, too. 4. No animation of the Bayes net is performed when an event sequence is run. Ideally, the diagram would be updated dynamically to show the activity, especially when posterior probabilities of nodes change and thresholds are exceeded. To use the BNT, do three kinds of development: A. create your own Bayes net whose input nodes correspond to pieces of evidence that might be presented and that might be relevant to drawing inferences about what's going on in the situation or process that you are analyzing. You do this by writing Python code that calls constructors etc. See the example in SampleNets.py. B. create a sample event stream that represents a plausible sequence of events that your system should be able to analyze. Put this in a file in the same format as used in sample-event-sequence.txt. C. modify the code of BNT or add new modules as necessary to obtain the functionality you want in your system. This could include code to perform actions whenever an output node's threshold is exceeded. It could include code to generate events (rather than read them from a file). And it could include code to describe more clearly what is going on whenever a node's probability is updated (e.g., what the significance of the update is -- more certainty about something, an indication that the weight of evidence is becoming strong, etc.)
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贝叶斯网络（Bayesian Network），也称为信念网络（Belief Network）或概率有向无环图（Probabilistic Directed Acyclic Graph，PDAG），是一种用图形表示概率模型的方法，它基于概率推断的贝叶斯定理。贝叶斯网络的背景可以追溯到 1980 年代初期，它是由一些著名的人工智能研究者如Judea Pearl、Peter Spirtes、Clark Glymour等提出和发展起来的。
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