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Table 6.2 Comparison of Low-Grade and undifferentiated stromal sarcomas Characteristic Growth rate 5-year survival Recurrence Sites of recurrence Cellular atypia Low-Grade ESS Indolent 80 100% 37 60% Local, distant (rare) Absent Undifferentiated ESS Aggressive 25 55% 50 85% Local, distant High-grade

McCume & BA Rattner (1992) Physiological and nonspeci c biomarkers In: (eds Hugget, RJ, RA Kimerle, PM Mehrle & HL Bergman) Biomarkers: Biochemical, Physiological, and Histological Markers of Anthropogenic Stress SETAC Special Publications Series, Lewis Publishers, pp 5 85 Mazeaud, MM, F Mazeaud & EM Donaldson (1977) Primary and secondary effects of stress in sh: some new data with a general review Transactions of the American Fisheries Society, 106, 201 212 McIntyre, AD (1973) Toxicity of methylmercury for steelhead trout sperm Bulletin of Environmental Contamination and Toxicology, 9, 98 McMaster, ME, GJ van der Kraak, CB Portt, KR Munkittrick, PK Sibley, IR Smith & DG.

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the original distribution. These advantages are the main motive for studying decompositions of relations in database theory [Maier 1983, Date 1986, Ullman 1988]. Therefore it is not surprising that database theory is closely connected to the theory of inference networks. The only difference is that the theory of inference networks focuses on reasoning, while database theory focuses on storing, maintaining, and retrieving data. However, being able to store a distribution more efficiently would not be of much use for reasoning tasks, were it not for the possibility of drawing inferences in the underlying domain using only the distributions { 1 , . . . , s } on the subspaces without having to reconstruct the original distribution . The basic idea is to pass information from subspace distribution to subspace distribution until all have been updated. This process is usually called evidence propagation. How it works is probably explained best by a simple example, which we present in the relational setting first. Later we study the probabilistic and finally the possibilistic case. There are, of course, even more types of inference networks for example, Dempster Shafer networks based on other calculi. However, these types are beyond the scope of this book.

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