In recent years, a variety of extensions and refinements have been developed
for data augmentation based model fitting routines. These developments aim to
extend the application, improve the speed and/or simplify the implementation of
data augmentation methods, such as the deterministic EM algorithm for mode
finding and stochastic Gibbs sampler and other auxiliary-variable based methods
for posterior sampling. In this overview article we graphically illustrate and
compare a number of these extensions, all of which aim to maintain the
simplicity and computation stability of their predecessors. We particularly
emphasize the usefulness of identifying similarities between the deterministic
and stochastic counterparts as we seek more efficient computational strategies.
We also demonstrate the applicability of data augmentation methods for handling
complex models with highly hierarchical structure, using a high-energy
high-resolution spectral imaging model for data from satellite telescopes, such
as the Chandra X-ray Observatory.Comment: Published in at http://dx.doi.org/10.1214/09-STS309 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
