A Comparison of Sequential Learning Methods for Incomplete Data
R. G. Cowell, A. P. Dawid, Paola Sebastiani · 1996
Abstract Deterministic and stochastic methods to approximate a mixture distribution which arises in learning with incomplete data are compared in a simple problem. Simulation results suggest that a simple deterministic method based on moment matching gives a very good approximation of the exact mixture distribution. This also works well when combined with an initial stochastic updating.