  In this work, we highlight a connection between the incremental proximal
method and stochastic filters. We begin by showing that the proximal operators
coincide, and hence can be realized with, Bayes updates. We give the explicit
form of the updates for the linear regression problem and show that there is a
one-to-one correspondence between the proximal operator of the least-squares
regression and the Bayes update when the prior and the likelihood are Gaussian.
We then carry out this observation to a general sequential setting: We consider
the incremental proximal method, which is an algorithm for large-scale
optimization, and show that, for a linear-quadratic cost function, it can
naturally be realized by the Kalman filter. We then discuss the implications of
this idea for nonlinear optimization problems where proximal operators are in
general not realizable. In such settings, we argue that the extended Kalman
filter can provide a systematic way for the derivation of practical procedures.
