设计处理随机变量均值的神经网络
Designing neural networks that process mean values of random variables
摘要：
我们介绍了一类神经网络来自概率模型的贝叶斯网络的形式。通过对网络中表示的概率模型的性质施加额外的假设，我们得到了具有标准动态的神经网络，这些神经网络不需要训练来确定突触权重，可以精确计算随机变量的平均值，可以汇集多个证据来源，并且可以干净一致地处理不一致或矛盾的证据。所提出的神经网络捕获贝叶斯网络的许多属性，提供分布式版本的概率模型。
英文摘要：
We introduce a class of neural networks derived from probabilistic models in the form of Bayesian networks. By imposing additional assumptions about the nature of the probabilistic models represented in the networks, we derive neural networks with standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the random variables, that can pool multiple sources of evidence, and that deal cleanly and consistently with inconsistent or contradictory evidence. The presented neural networks capture many properties of Bayesian networks, providing distributed versions of probabilistic models.
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