Data Augmentation: Translation of Common Sense into Reality
Methods introduced in the past followed the Monte Carlo approach for
computing posterior distribution. On the contrary, DA allows approximate
Bayesian analysis with a standard maximum likelihood function. Its
philosophy is to translate prior information as equivalent data and add
this external information to the observed study data then conventional
methods of frequentist can be applied. No specific tools are required to
compute posterior mean and variance; inverse-variance weighted averaging
is a rule of thumb for estimation (6).
This technique provides an effective remedy to treat bias estimation
caused by data sparseness (7-12). In fact,
it considers prior information as a penalty for maximum likelihood
estimates and approximates posterior mode and variance.