  The analysis of diffusion processes in real-world propagation scenarios often
involves estimating variables that are not directly observed. These hidden
variables include parental relationships, the strengths of connections between
nodes, and the moments of time that infection occurs. In this paper, we propose
a framework in which all three sets of parameters are assumed to be hidden and
we develop a Bayesian approach to infer them. After justifying the model
assumptions, we evaluate the performance efficiency of our proposed approach
through numerical simulations on synthetic datasets and real-world diffusion
processes.
