aka imputation
Inferring values of missing entries in a matrix of data.
See here for definition
An interesting example of an imputation-like task is known as image inpainting. The goal is to “fill in” holes (e.g., due to scratches or occlusions) in an image with realistic texture
This can be tackled by building a joint probability model of the pixels, given a set of clean images, and then inferring the unknown variables (pixels) given the known variables (pixels). This is somewhat like masket basket analysis, except the data is real-valued and spatially structured, so the kinds of probability models we use are quite different. See Sections 19.6.2.7 and 13.8.4 for some possible choices.
Another interesting example of an imputation-like task is known as collaborative filtering. A common example of this concerns predicting which movies people will want to watch based on how they, and other people, have rated movies which they have already seen. The key idea is that the prediction is not based on features of the movie or user (although it could be), but merely on a ratings matrix.
This is often used in Recommender systems