Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Simulated annealing & boltzmann Machines: a stochastic approach to combinatorialoptimization & neural computing
Neurocomputing: foundations of research
Neurocomputing: foundations of research
Gibbs sampling in Bayesian networks (research note)
Artificial Intelligence
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
A method of computing generalized Bayesian probability values for expert systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
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The EM-algorithm is a general procedure to get maximum likelihood estimates if part of the observations on the variables of a network are missing. In this paper a stochastic version of the algorithm is adapted to probabilistic neural networks describing the associative dependency of variables. These networks have a probability distribution, which is a special case of the distribution generated by probabilistic inference networks. Hence both types of networks can be combined allowing to integrate probabilistic rules as well as unspecified associations in a sound way. The resulting network may have a number of interesting features including cycles of probabilistic rules, hidden 'unobservable' variables, and uncertain and contradictory evidence.