Multiply sectioned Bayesian networks for neuromuscular diagnosis
نویسندگان
چکیده
منابع مشابه
Justifying Multiply Sectioned Bayesian Networks
We consider multiple agents who s task is to determine the true state of a uncertain domain so they can act properly If each agent only has partial knowledge about the domain and local observation how can agents accomplish the task with the least amount of commu nication Multiply sectioned Bayesian networks MSBNs provide an e ective and exact framework for such a task but also impose a set of c...
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Multiply sectioned Bayesian networks (MSBNs) are extension of Bayesian networks for flexible modeling and cooperative multiagent probabilistic inference. A large and complex equipment is modeled by a set of Bayesian subnets in a MSBN each of which corresponds to a natural component of the equipment. Each subnet forms the core knowledge of an autonomous M&D agent. Inference is performed in a dis...
متن کاملWhat Necessitate Multiply Sectioned Bayesian Networks?
Multiply sectioned Bayesian networks (MSBNs) provide a coherent framework for probabilistic reasoning in cooperative multi-agent distributed interpretation systems (CMADISs). Previous work on MSBNs fo-cuses on the suuciency of MSBNs for representation and inference with uncertain knowledge in CMADISs. Since several representation choices were made in the formation of a MSBN, it appears unclear ...
متن کاملComparing Alternative Methods for Inference in Multiply Sectioned Bayesian Networks
Multiply sectioned Bayesian networks (MSBNs) provide one framework for agents to estimate the state of a domain. Existing methods for multi-agent inference in MSBNs are based on linked junction forests (LJFs). The methods are extensions of message passing in junction trees for inference in singleagent Bayesian networks (BNs). We consider extending other inference methods in single-agent BNs to ...
متن کاملComparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks
Multiply sectioned Bayesian networks (MSBNs) were originally proposed as a modular representation of uncertain knowledge by sectioning a large Bayesian network (BN) into smaller units. More recently, hierarchical Markov networks (HMNs) were developed in part as an hierarchical representation of the flat BN. In this paper, we compare the MSBN and HMN representations. The MSBN representation does...
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ژورنال
عنوان ژورنال: Artificial Intelligence in Medicine
سال: 1993
ISSN: 0933-3657
DOI: 10.1016/0933-3657(93)90019-y