问题描述
任何人都可以推荐用Python实现的贝叶斯信念网络分类器，该分类器可以基于稀疏网络的输入来生成信念概率，该稀疏网络描述了有关几个相互关联的对象的一系列事实.
Can anyone recommend a Bayesian belief network classifier implemented in Python that can generate a probability of belief based on the input of a sparse network describing a series of facts about several inter-related objects?
例如鉴于事实以FOL的形式表达了"X饿了，是猴子又吃了"的事实，
e.g. given the facts "X is hungry, is a monkey and eats" formulated in FOL like:
isHungry(x) ^ isMonkey(x) ^ eats(x,y)
以及像这样的训练语料库:
as well as a training corpus like:
isHungry(a) ^ isMonkey(a) ^ eats(a,b) => true
isHungry(b) ^ ~isMonkey(b) ^ eats(b,c) => true
isMonkey(d) ^ eats(d,e) => true
isMonkey(f) ^ eats(f,g) => false
isMonkey(h) ^ ~eats(h,i) => true
isBanana(j) ^ ~eats(j,k) => true
我想在语料库上训练贝叶斯信念网络，并用它来估计事实的信念概率.
I'd like to train a Bayesian belief network on the corpus, and use it to estimate the belief probability of the facts.
请注意，我不是在说天真"贝叶斯文本分类器.
Note, I'm not talking about Naive Bayesian text classifiers.
推荐答案
Python Bayes网络工具箱将是一个很好的起点.
The Python Bayes Network Toolbox would be a good starting point.
此外，还有一个更通用的贝叶斯推理工具包，名为 bayespy .
In addition, there is this more generic Bayesian inference tools package, named bayespy .
高度.
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