Probabilistic reasoning and probabilistic neural networks
Gerhard Pass · International Journal of Intelligent Systems · 1992
The Boltzmann machine is a probabilistic neural network describing the associative dependency of variables. It yields 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, and hidden “unobservable” variables. the maximum likelihood approach is used to combine possibly conflicting pieces of information on rules or associations.