---
title: "MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings"
authors:
  - "Steen Andreassen"
  - "Marianne Woldbye"
  - "Björn Falck"
  - "Stig K. Andersen"
doi: ""
url: "https://ijcai.org/proceedings/1987/76"
pdf: "https://ijcai.org/proceedings/1987/0076.pdf"
venue: "Proceedings of the Tenth International Joint Conference on Artificial Intelligence"
year: 1987
pages: "366-371"
track: "Knowledge Representation"
keywords:
---

# MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings

Steen Andreassen, Marianne Woldbye, Björn Falck, Stig K. Andersen

*Proceedings of the Tenth International Joint Conference on Artificial Intelligence*, pages 366-371.

## 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 knowledge" in the form of pathophysiological models are used to reduce the problem of estimating thousands of conditional probabilities to a rnanageble 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.