Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A munin network for the median nerve-a case study on loops
Applied Artificial Intelligence
Causal Probabilistic Networks with Both Discrete and Continuous Variables
IEEE Transactions on Pattern Analysis and Machine Intelligence
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Planning of therapy and tests in causal probabilistic networks
Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
Multilevel Bayesian networks for the analysis of hierarchical health care data
Artificial Intelligence in Medicine
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Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between nodes. Various types of models are described and a program that handles such models is presented. Large causal probabilistic networks often contain several copies of identical tables or structures. A header facility that provides common definitions of such repeated elements is proposed. This facility makes specifications much shorter and easier to construct and maintain.