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Foundations for Bayesian networks
, 2001
"... Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probabi ..."
Abstract

Cited by 11 (7 self)
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Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current foundations are problematic, and put forward new foundations which involve aspects of both the interpreted and the formal approaches. One standard approach is to interpret a Bayesian network objectively: the graph in a Bayesian network represents causality in the world and the specified probabilities are objective, empirical probabilities. Such an interpretation founders when the Bayesian network independence assumption (often called the causal Markov condition) fails to hold. In §2 I catalogue the occasions when the independence assumption fails, and show that such failures are pervasive. Next, in §3, I show that even where the independence assumption does hold objectively, an agent’s causal knowledge is unlikely to satisfy the assumption with respect to her subjective probabilities, and that slight differences between an agent’s subjective Bayesian network and an objective Bayesian network can lead to large differences between probability distributions determined by these networks. To overcome these difficulties I put forward logical Bayesian foundations in §5. I show that if the graph and probability specification in a Bayesian network are thought of as an agent’s background knowledge, then the agent is most rational if she adopts the probability distribution determined by the
Constructing a Topic Association Network based on Random Graph
"... With the huge amount of information available online, the information sharing in the World Wide Web (Web) tends to be explosive. This makes it difficult to find valuable information on the Web. Users are easily facing information overload and lost in the information resource.The motivation of this r ..."
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With the huge amount of information available online, the information sharing in the World Wide Web (Web) tends to be explosive. This makes it difficult to find valuable information on the Web. Users are easily facing information overload and lost in the information resource.The motivation of this research is to develop an adapter system which is built upon the traditional search engine to achieve the ”intelligent search ” such that: (1) it will distinguish the semantic background of the same topics since identical topic name may imply different meaning under different semantic background; (2) it will return all the topics which are significantly correlated to the queried topics. We believe that a wellconstructed Topic Association Network (TAN) will improve the process of the knowledge extraction in the web and also facilitate knowledge learning. 1