The assertion was not that these examples are common rather that currently they are more common to generic app developers than manipulating PDE's As K-filters

The assertion was not that these examples are common rather that currently they are more common to generic app developers than manipulating PDE's As K-filters in data processing and interpretation, that depends thoroughly on the data domains, a good number have biases and co-signals that are more easily removed with an adaptive model of some form. Eg: magnetic heading effect when recording nine axis nano-tesla range ground signals. The readings returned over a specific point at a specific time of day are a function of sensor speed and heading. Repeated flying over the same point (hypothetically at the same time) from North to South Vs East to West returns different data streams on each of the nine channels. To get a "true ground reading" both the heading bias and the diurnal flux must be estimated and subtracted. > plenty of other imputation approaches that are simpler/better/more accessible. Do tell. What would you use in the above example?