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.