  This paper proposes a novel technique to prove a one-shot version of
achievability results in network information theory. The technique is not based
on covering and packing lemmas. In this technique, we use an stochastic encoder
and decoder with a particular structure for coding that resembles both the ML
and the joint-typicality coders. Although stochastic encoders and decoders do
not usually enhance the capacity region, their use simplifies the analysis. The
Jensen inequality lies at the heart of error analysis, which enables us to deal
with the expectation of many terms coming from stochastic encoders and decoders
at once. The technique is illustrated via several examples: point-to-point
channel coding, Gelfand-Pinsker, Broadcast channel (Marton), Berger-Tung,
Heegard-Berger/Kaspi, Multiple description coding and Joint source-channel
coding over a MAC. Most of our one-shot results are new. The asymptotic forms
of these expressions is the same as that of classical results. Our one-shot
bounds in conjunction with multi-dimensional Berry-Essen CLT imply new results
in the finite blocklength regime. In particular applying the one-shot result
for the memoryless broadcast channel in the asymptotic case, we get the entire
region of Marton's inner bound without any need for time-sharing.
