Washington: Networks multiply
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...
متن کاملUpdating Probabilities in Multiply-Connected Belief Networks
This paper focuses on probability updates in multiply-connected belief networks. Pearl has designed the method of conditioning, which enables us to apply his algorithm for belief updates in singly-connected networks to multiply-connected belief networks by selecting a loop-cutset for the network and instantiating these loop-cutset nodes. We discuss conditions that need to be satisfied by the se...
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 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...
متن کاملProbabilistic inference in multiply connected belief networks using loop cutsets
The method of conditioning permits probabilistic inference in multiply connected belief networks using an algorithm by Pearl. This method uses a select set of nodes, the loop cutset, to render the multiply connected network singly connected. We discuss the function of the nodes of the loop cutset and a condition that must be met by the nodes of the loop cutset. We show that the problem of findi...
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ژورنال
عنوان ژورنال: Nature
سال: 1987
ISSN: 0028-0836,1476-4687
DOI: 10.1038/328753b0