  The design of sampling set (DoS) for bandlimited graph signals (GS) has been
extensively studied in recent years, but few of them exploit the benefits of
the stochastic prior of GS. In this work, we introduce the optimization
framework for Bayesian DoS of bandlimited GS. We also illustrate how the choice
of different sampling sets affects the estimation error and how the prior
knowledge influences the result of DoS compared with the non-Bayesian DoS by
the aid of analyzing Gershgorin discs of error metric matrix. Finally, based on
our analysis, we propose a heuristic algorithm for DoS to avoid solving the
optimization problem directly.
