Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Neural Computation
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Neural belief networks (NBNs) are neural network models derived from the hypothesis that populations of neurons perform statistical inference. Such networks can be generated from a broad class of probabilistic models, but often function through the multiplication of neural firing rates. By introducing additional assumptions about the nature of the probabilistic models, we derive a class of neural networks that function only through weighted sums of neural activities.