Position Fixing and Uncertainty

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 up grading 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 identi fied 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