  Mirror Descent is a popular algorithm, that extends Gradients Descent (GD)
beyond the Euclidean geometry. One of its benefits is to enable strong
convergence guarantees through smooth-like analyses, even for objectives with
exploding or vanishing curvature. This is achieved through the introduction of
the notion of relative smoothness, which holds in many of the common use-cases
of Mirror descent. While basic deterministic results extend well to the
relative setting, most existing stochastic analyses require additional
assumptions on the mirror, such as strong convexity (in the usual sense), to
ensure bounded variance. In this work, we revisit Stochastic Mirror Descent
(SMD) proofs in the (relatively-strongly-) convex and relatively-smooth
setting, and introduce a new (less restrictive) definition of variance which
can generally be bounded (globally) under mild regularity assumptions. We then
investigate this notion in more details, and show that it naturally leads to
strong convergence guarantees for stochastic mirror descent. Finally, we
leverage this new analysis to obtain convergence guarantees for the Maximum
Likelihood Estimator of a Gaussian with unknown mean and variance.
