贝叶斯和神经网络之间有什么关系？
我正在寻找使用 CUDA 实现的计算量大的任务，并想知道神经网络或贝叶斯网络是否适用。 但这不是我的问题，而是两种网络类型之间的关系是什么。 它们似乎非常相关，特别是如果您查看具有学习能力的贝叶斯网络（维基百科上的文章提到）。 乍一看，贝叶斯网络有点像特定类型的神经网络。 谁能总结一下他们的关系，除了表面的相似性之外是否还有其他联系？
I'm looking for computationally heavy tasks to implement with CUDA and wonder if neural networks or bayesian networks might apply. This is not my question, though, but rather what the relation between the two network types is. They seem very related, especially if you look at bayesian networks with a learning capability (which the article on wikipedia mentions). At a glance, bayesian networks look at bit like a specific type of neural networks. Can anyone sum up their relationship, and if there is any connection beyond the apparent similarity?
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贝叶斯网络表示变量之间的独立（和依赖）关系。 因此，链接代表概率意义上的条件关系。 一般来说，神经网络没有这样的直接解释，事实上，大多数神经网络的中间节点都是发现的特征，而不是有任何与它们本身相关的谓词。
Bayesian networks represent independence (and dependence) relationships between variables. Thus, the links represent conditional relationships in the probabilistic sense. Neural networks, generally speaking, have no such direct interpretation, and in fact the intermediate nodes of most neural networks are discovered features, instead of having any predicate associated with them in their own right.
据报道，贝叶斯网络对某些神经网络中出现的“过度训练”具有更强的抵抗力。 换句话说，一些神经网络对训练中使用的观察到的测量结果进行了如此“训练”，以至于它们对于一般情况没有用处。
It is reported that Bayesian networks are more resistant to the "overtraining" that is seen in some neural networks. In other words some neural networks become so "trained" to the observed measurements used in training that they aren't useful for the general cases.
确实如此。 我将贝叶斯网络视为大规模应用贝叶斯定理的神经网络，但我不记得细节了。 我知道你可以在哪里找到它们，为此我推荐这本书。
Indeed they are. I see a bayesian network as a neural network applying the Baye's Theorem on large scale, but I don't remember details. I know where you can find them and I recommend this book for that.