  Amplitude analysis is a powerful technique to study hadron decays. A
significant complication in these analyses is the treatment of instrumental
effects, such as background and selection efficiency variations, in the
multidimensional kinematic phase space. This paper reviews conventional methods
to estimate efficiency and background distributions and outlines the methods of
density estimation using Gaussian processes and artificial neural networks.
Such techniques see widespread use elsewhere, but have not gained popularity in
use for amplitude analyses. Finally, novel applications of these models are
proposed, to estimate background density in the signal region from the
sidebands in multiple dimensions, and a more general method for model-assisted
density estimation using artificial neural networks.
