Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm
📝 Original Info
- Title: Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm
- ArXiv ID: 1303.5737
- Date: 2013-03-26
- Authors: Researchers from original ArXiv paper
📝 Abstract
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.💡 Deep Analysis
Deep Dive into Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm.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.