Data Mining: Foundations and Intelligent Paradigms
Volume 3: Medical, Health, Social, Biological and other Applications
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This chapter explores the theory of Bayesian networks with particular reference to Maximum Entropy Formalism. A discussion of objective Bayesianism is given together with some brief remarks on applications.
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This chapter presents a summary of a sample of research in the field of biomedical informatics. The topics include digital health research, medical decision support systems, Bayesian networks, tele-monitoring,...
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Volume 3: Medical, Health, Social, Biological and other Applications
Book
Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects
Book
Volume 1: Clustering, Association and Classification
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As discussed in the previous volume, the term Data Mining grew from the relentless growth of techniques used to interrogation masses of data. As a myriad of databases emanated from disparate industries, enterpris...
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The human body is composed of eleven sub-systems. These include the: respiratory, digestive, muscular, immune, circulatory, digestive, skeletal, endocrine, urinary, integumentary and reproductive systems [1]. ...
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The term Data Mining grew from the relentless growth of techniques used to interrogation masses of data. As a myriad of databases emanated from disparate industries, management insisted their information officers...
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Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry, medicine and finance. Bayesian networks provide a theoretical framework for dealing...
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The author’s past work in this area has shown that the probability of a state of a Bayesian network, found using the standard Bayesian techniques, could be equated to the Maximum Entropy solution and that this...