MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings
Abstract
Experience gained through building a causal network for interpretation of electromyographic findings has shown that probabilistic inference is a realistic possibility in networks of non-trivial size. The use of nodes with many internal states has made it possible to make a conceptually simple and compact representation of knowledge. Deep in the form of pathophysiological models are used to reduce the problem of estimating thousands of conditional probabilities to a manageble size. The network has built-in mechanisms that will detect when the network is confronted with a situation outside the limits of its own knowledge and it handles conflicting evidence in a simple and consistent way.
Cite
Text
Andreassen et al. "MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings." International Joint Conference on Artificial Intelligence, 1987.Markdown
[Andreassen et al. "MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings." International Joint Conference on Artificial Intelligence, 1987.](https://mlanthology.org/ijcai/1987/andreassen1987ijcai-munin/)BibTeX
@inproceedings{andreassen1987ijcai-munin,
title = {{MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings}},
author = {Andreassen, Steen and Woldbye, Marianne and Falck, Björn and Andersen, Stig K.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1987},
pages = {366-372},
url = {https://mlanthology.org/ijcai/1987/andreassen1987ijcai-munin/}
}