  In this work, we propose a hybrid Bayesian approach towards clock offset and
skew estimation, thereby synchronizing large scale networks. In particular, we
demonstrate the advantage of Bayesian Recursive Filtering (BRF) in alleviating
time-stamping errors for pairwise synchronization. Moreover, we indicate the
benefit of Factor Graph (FG), along with Belief Propagation (BP) algorithm in
achieving high precision end-to-end network synchronization. Finally, we reveal
the merit of hybrid synchronization, where a large-scale network is divided
into local synchronization domains, for each of which a suitable
synchronization algorithm (BP- or BRF-based) is utilized. The simulation
results show that, despite the simplifications in the hybrid approach, the Root
Mean Square Errors (RMSEs) of clock offset and skew estimation remain below 5
ns and 0.3 ppm, respectively.
