  There is an overwhelmingly large literature and algorithms already available
on `large scale inference problems' based on different modeling techniques and
cultures. Our primary goal in this paper is \emph{not to add one more new
methodology} to the existing toolbox but instead (a) to clarify the mystery how
these different simultaneous inference methods are \emph{connected}, (b) to
provide an alternative more intuitive derivation of the formulas that leads to
\emph{simpler} expressions, and (c) to develop a \emph{unified} algorithm for
practitioners. A detailed discussion on representation, estimation, inference,
and model selection is given. Applications to a variety of real and simulated
datasets show promise. We end with several future research directions.
