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13
Approximating Discrete Probability Distributions With Bayesian Networks
 in Proceedings of the International Conference on Artificial Intelligence in Science and Technology
, 2000
"... I generalise the arguments of [Chow & Liu 1968] to show that a Bayesian network satisfying some arbitrary constraint that best approximates a probability distribution is one for which mutual information weight is maximised. I give a practical procedure for nding an approximation network and e ..."
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Cited by 12 (3 self)
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I generalise the arguments of [Chow & Liu 1968] to show that a Bayesian network satisfying some arbitrary constraint that best approximates a probability distribution is one for which mutual information weight is maximised. I give a practical procedure for nding an approximation network and evaluate its application on a range of data sets. Articial intelligence requires the ability to reach conclusions that may be far from certain. For example an expert system for medical diagnosis may be given the symptoms of some patient and asked to provide a diagnosis  even though the background knowledge and symptom information may not be enough to determine for sure which problem actually besets the patient. Probability theory provides a plausible model for reasoning under uncertainty, since one would expect a diagnosis to be relatively probable, given the symptoms. This paper addresses practical issues to do with the implementation of probabilistic reasoning. The plan is rst to discuss...
Recursive causality in bayesian networks and selffibring networks
 Laws and Models in Science. King’s
, 2004
"... Causal relations can themselves take part in causal relations. The fact that smoking causes cancer (), for instance, causes government to restrict tobacco advertising (), which helps prevent smoking (), which in turn helps prevent cancer (). This causal chain is depicted in Figure 1, and further exa ..."
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Cited by 7 (5 self)
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Causal relations can themselves take part in causal relations. The fact that smoking causes cancer (), for instance, causes government to restrict tobacco advertising (), which helps prevent smoking (), which in turn helps prevent cancer (). This causal chain is depicted in Figure 1, and further examples will be given in
Machine Learning and the Philosophy of Science: a Dynamic Interaction
 In Proceedings of the ECMLPKDD01 Workshop on Machine Leaning as Experimental Philosophy of Science
, 2001
"... I posit here a dynamic interaction between machine learning and the philosophy of science, and illustrate this claim with the use of a case study involving the foundations of Bayesian networks. ..."
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Cited by 4 (1 self)
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I posit here a dynamic interaction between machine learning and the philosophy of science, and illustrate this claim with the use of a case study involving the foundations of Bayesian networks.
Bayesian networks for logical reasoning
 in Proceedings of the AAAI Fall Symposium on Using Uncertainty in Computation
, 2001
"... By identifying and pursuing analogies between causal and logical influence I show how the Bayesian network formalism can be applied ..."
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Cited by 2 (0 self)
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By identifying and pursuing analogies between causal and logical influence I show how the Bayesian network formalism can be applied
Learning Causal Relationships
 LSE CENTRE FOR NATURAL AND SOCIAL SCIENCES, WWW.LSE.AC.UK/DEPTS/CPNSS/PROJ_CAUSALITY.HTM
, 2002
"... ..."
Article Calculating the Prior Probability Distribution for a Causal Network Using Maximum Entropy: Alternative Approaches
, 2011
"... entropy ..."
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Learning Causal Bayesian Networks from Literature Data
, 2006
"... In biomedical domains free text electronic literature is an important resource for knowledge discovery and acquisition. It is particularly true in the context of data analysis, where it provides a priori components to enhance learning, or references for evaluation. The biomedical literature contains ..."
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In biomedical domains free text electronic literature is an important resource for knowledge discovery and acquisition. It is particularly true in the context of data analysis, where it provides a priori components to enhance learning, or references for evaluation. The biomedical literature contains the rapidly accumulating, voluminous collection of scientific observations boosted by the new highthroughput measurement technologies. The broader context of our work is to support statistical inference about the structural properties of the domain model. This is a twostep process, which consists of (1) the reconstruction of the beliefs over mechanisms from the literature by learning generative models and (2) their usage in a subsequent learning phase. To automate the extraction of this prior knowledge we discuss the types of uncertainties in a domain with respect to causal mechanisms and introduce a hypothesis about certain structural faithfulness between the causal Bayesian network model of the domain and a binary Bayesian network representing occurrences (i.e. causal relevance) of domain entities in publications describing causal relations. Based on this hypothesis, we propose various generative probabilistic models for the occurrences of biomedical concepts in scientific papers. Finally, we investigate how Bayesian network learning with minimal linguistic analysis support can be applied to discover and extract causal dependency domain models from the domain literature.
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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.