Interpolations

Interpolations refer to the methods of estimating unknown values within a data set, typically by deriving a function that fits the known data points. These methods are used in various fields, including mathematics, computer science, and statistics, to predict or fill in gaps within a dataset. They rely on the assumption that the data exhibits some form of pattern or relationship, such as linearity or smoothness. The goal of interpolation is to create a continuous and plausible approximation of the function that generated the available data. Various techniques exist, each with different levels of accuracy and computational complexity. The choice of interpolation method depends on the specific data characteristics and the desired level of precision.

Interpolations meaning with examples

• The weather model used sophisticated mathematical interpolations to estimate temperatures between the recorded observation points. These predictions filled in data gaps and created a smooth temperature map, useful for forecasting.
• When generating images, the graphic artist used linear interpolations to smoothly transition between keyframes, creating the illusion of continuous movement for objects and characters in the animation.
• In the survey data, gaps were filled using interpolations; this enabled the generation of an overall population opinion based upon all the input information.
• A spline interpolation was used to create a curve that passed through each data point to accurately represent the function that produced the measured readings in the experiment.