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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 ..."
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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
A New Definition of Qualified Gain in a Data Fusion Process: Application to Telemedicine
, 2002
"... A formal framework is proposed for defining data fusion processes and particularly a notion of qualified gain in a data fusion process is proposed: gain in representation, completeness, accuracy and certainty. These notions are applied to a medical monitoring and diagnosis problem where a dynamic Ba ..."
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Cited by 2 (0 self)
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A formal framework is proposed for defining data fusion processes and particularly a notion of qualified gain in a data fusion process is proposed: gain in representation, completeness, accuracy and certainty. These notions are applied to a medical monitoring and diagnosis problem where a dynamic Bayesian network (DBN) is used to modelize time se ries of observations and evolving states. The model aims at giving a daily diagnosis. Our experiments are under way by using data of an already existing system collected on kidney disease patients. Results will be characterized using our notion of qualified gains.
Designing Smart Agent Based Telemedicine with Dynamic Bayesian Networks: An Application to Kidney Disease People
, 2002
"... Telemedicine is the delivery of healthcare services, where distance is a critical factor. The use of smart agent and artificial intelligence techniques to enhance such services is proposed through a description of the needs and goals of a smart agent based telemedicine system. A realworld example ..."
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Cited by 1 (1 self)
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Telemedicine is the delivery of healthcare services, where distance is a critical factor. The use of smart agent and artificial intelligence techniques to enhance such services is proposed through a description of the needs and goals of a smart agent based telemedicine system. A realworld example is presented and the use of dynamic bayesian networks (DBN) is promoted because DBN forms a wellsuited formal framework to deal with uncertainty and stochactic processes which are characteristic of the domain of telemedicine. The model given in our realworld application aims at giving a daily diagnosis on the hydration state of kidney disease people. Our experiments are under way by using data of an already existing system.
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"... Metabolism is a set of chemical reactions, used by living organisms to process chemical compounds in order to take energy and eliminate toxic compounds, for example. Its processes are referred as metabolic pathways. Understanding metabolism is imperative to biology, toxicology and medicine, but the ..."
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Metabolism is a set of chemical reactions, used by living organisms to process chemical compounds in order to take energy and eliminate toxic compounds, for example. Its processes are referred as metabolic pathways. Understanding metabolism is imperative to biology, toxicology and medicine, but the number and complexity of metabolic pathways makes this a difficult task. In our paper, we investigate the use of causal Bayesian networks to model the pathways of yeast saccharomyces cerevisiae metabolism: such a network can be used to draw predictions about the levels of metabolites and enzymes in a particular specimen. We, propose a twostage methodology for causal networks, as follows. First construct a causal network from the network of metabolic pathways. The viability of this causal network depends on the validity of the causal Markov condition. If this condition fails, however, the principle of the common cause motivates the addition of a new causal arrow or a new `hidden ' common cause to the network (stage 2 of the model formation process). Algorithms for adding arrows or hidden nodes have been developed separately in a number of papers, and in this paper we combine them, showing how the resulting procedure can be applied to the metabolic pathway problem. Our general approach was tested on neural cell morphology data and demonstrated noticeable improvements in both prediction and network accuracy.