ABSTRACT: Taken random observations are usually accompanied by rectified knowledge regarding their behaviour. In modern computer applications, raw data sets are usually exploited at learning phase. At this stage, available data are explored in order to extract necessary parameters required within the inference scheme computations. Crude data processing enables conditional dependencies extraction. It starts with upgrading histograms and their uncertainty estimation. Exploiting principles of fuzzy systems one can obtain modified step-wise structure in the form of locally injective density functions. They can be perceived as conditional dependency diagrams with identified uncertainty that enables constructing basic probability assignments. Belief, uncertainty and plausibility measures are extracted from initial raw data sets. The paper undertakes problem of belief structures upgraded from uncertainty model in order to solve the position fixing problem. The author intention is presenting position fixing as an inference scheme. The scheme engages evidence, hypothesis and revokes concept of conditional relationships.

KEYWORDS: Uncertainty Models, Nautical Evidence, Conditional Dependencies, Histograms Transformations, Belief Assignments, Schemes of Reasoning, Combining Evidence, Hypotheses Selection

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