In this paper we generalize the estimation-control duality that exists in the
linear-quadratic-Gaussian setting. We extend this duality to maximum a posteriori
estimation of the system's state, where the measurement and dynamical system noise are
independent log-concave random variables. More generally, we show that a problem which
induces a convex penalty on noise terms will have a dual control problem. We provide
conditions for strong duality to hold, and then prove relaxed conditions for the piecewise
linear-quadratic case. The results have applications in estimation problems with nonsmooth
densities, such as log-concave maximum likelihood densities. We conclude with an example
reconstructing optimal estimates from solutions to the dual control problem, which has
implications for sharing solution methods between the two types of problems.