Local learning in probabilistic networks with hidden variables

Authors:
Stuart Russell;John Binder;Daphne Koller;Keiji Kanazawa
Affiliations:
Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA
Venue:
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Year:
1995

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Abstract

Probabilistic networks which provide compact descriptions of complex stochastic relationships among several random variables are rapidly becoming the tool of choice for uncertain reasoning in artificial intelligence. We show that networks with fixed structure containing hidden variables can be learned automatically from data using a gradient-descent mechanism similar to that used in neural networks We also extend the method to networks with intensionally represented distributions, including networks with continuous variables and dynamic probabilistic networks Because probabilistic networks provide explicit representations of causal structure human experts can easily contribute pnor knowledge to the training process, thereby significantly improving the learning rate Adaptive probabilistic networks (APNs) may soon compete directly with neural networks as models in computational neuroscience as well as in industrial and financial applications.