  We observe an undirected graph $G$ without multiple edges and self-loops,
which is to represent a protein-protein interaction (PPI) network. We assume
that $G$ evolved under the duplication-mutation with complementarity (DMC)
model from a seed graph, $G_0$, and we also observe the binary forest $\Gamma$
that represents the duplication history of $G$. A posterior density for the DMC
model parameters is established, and we outline a sampling strategy by which
one can perform Bayesian inference; that sampling strategy employs a particle
marginal Metropolis-Hastings (PMMH) algorithm. We test our methodology on
numerical examples to demonstrate a high accuracy and precision in the
inference of the DMC model's mutation and homodimerization parameters.
