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This lecture explores deterministic interpolation methods, categorizing them into global approaches, which consider all measurement points in a domain, and local approaches, which only consider a limited number of neighboring points. While global methods are often too simplistic, local methods, such as nearest neighbor and weighted moving average, are preferred despite their subjective nature and tendency to produce arbitrary results with high uncertainty.