  Real-world control applications often involve complex dynamics subject to
abrupt changes or variations. Markov jump linear systems (MJS) provide a rich
framework for modeling such dynamics. Despite an extensive history, theoretical
understanding of parameter sensitivities of MJS control is somewhat lacking.
Motivated by this, we investigate robustness aspects of certainty equivalent
model-based optimal control for MJS with quadratic cost function. Given the
uncertainty in the system matrices and in the Markov transition matrix is
bounded by $\epsilon$ and $\eta$ respectively, robustness results are
established for (i) the solution to coupled Riccati equations and (ii) the
optimal cost, by providing explicit perturbation bounds which decay as
$\mathcal{O}(\epsilon + \eta)$ and $\mathcal{O}((\epsilon + \eta)^2)$
respectively.
